{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Outlier, adversarial and drift detection on CIFAR10\n",
    "\n",
    "* [1. Dataset](#1.-Dataset)\n",
    "\n",
    "* [2. Outlier detection with a variational autoencoder (VAE)](#2.-Outlier-detection-with-a-variational-autoencoder-(VAE))\n",
    "\n",
    "* [3. Adversarial detection by matching prediction probabilities](#3.-Adversarial-detection-by-matching-prediction-probabilities)\n",
    "\n",
    "* [4. Drift detection with Kolmogorov-Smirnov](#4.-Drift-detection-with-Kolmogorov-Smirnov)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Dataset\n",
    "\n",
    "[CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) consists of 60,000 32 by 32 RGB images equally distributed over 10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship and truck."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train:  (50000, 32, 32, 3) (50000,)\n",
      "Test:  (10000, 32, 32, 3) (10000,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 16 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# imports and plot examples\n",
    "import os\n",
    "os.environ[\"TF_USE_LEGACY_KERAS\"] = \"1\"\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import tensorflow as tf\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
    "X_train = X_train.astype('float32') / 255\n",
    "X_test = X_test.astype('float32') / 255\n",
    "y_train = y_train.astype('int64').reshape(-1,)\n",
    "y_test = y_test.astype('int64').reshape(-1,)\n",
    "print('Train: ', X_train.shape, y_train.shape)\n",
    "print('Test: ', X_test.shape, y_test.shape)\n",
    "\n",
    "plt.figure(figsize=(10, 10))\n",
    "n = 4\n",
    "for i in range(n ** 2):\n",
    "    plt.subplot(n, n, i + 1)\n",
    "    plt.imshow(X_train[i])\n",
    "    plt.axis('off')\n",
    "plt.show();"
   ]
  },
  {
   "attachments": {
    "vae_lillog.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Outlier detection with a variational autoencoder (VAE)\n",
    "\n",
    "### Method\n",
    "\n",
    "In a nutshell:\n",
    "\n",
    "- Train a VAE on *normal* data so it can reconstruct *inliers* well\n",
    "- If the VAE cannot reconstruct the incoming requests well? Outlier!\n",
    "\n",
    "More resources on VAE: [paper](https://arxiv.org/abs/1312.6114), [excellent blog post](https://lilianweng.github.io/lil-log/2018/08/12/from-autoencoder-to-beta-vae.html#vae-variational-autoencoder)\n",
    "\n",
    "![vae-lillog.png](attachment:vae_lillog.png)\n",
    "\n",
    "*Image source: https://lilianweng.github.io/lil-log/2018/08/12/from-autoencoder-to-beta-vae.html*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# more imports\n",
    "import logging\n",
    "import numpy as np\n",
    "import os\n",
    "\n",
    "from tensorflow.keras.layers import Conv2D, Conv2DTranspose, Dense\n",
    "from tensorflow.keras.layers import Flatten, Layer, Reshape, InputLayer\n",
    "from tensorflow.keras.regularizers import l1\n",
    "\n",
    "from alibi_detect.od import OutlierVAE\n",
    "from alibi_detect.utils.fetching import fetch_detector\n",
    "from alibi_detect.utils.perturbation import apply_mask\n",
    "from alibi_detect.saving import save_detector, load_detector\n",
    "from alibi_detect.utils.visualize import plot_instance_score, plot_feature_outlier_image\n",
    "\n",
    "logger = tf.get_logger()\n",
    "logger.setLevel(logging.ERROR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load detector or train from scratch\n",
    "\n",
    "The pretrained outlier and adversarial detectors used in the notebook can be found [here](https://console.cloud.google.com/storage/browser/seldon-models/alibi-detect). You can use the built-in ```fetch_detector``` function which saves the pre-trained models in a local directory ```filepath``` and loads the detector. Alternatively, you can train a detector from scratch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_pretrained = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "filepath = os.path.join(os.getcwd(), 'outlier')\n",
    "detector_type = 'outlier'\n",
    "dataset = 'cifar10'\n",
    "detector_name = 'OutlierVAE'\n",
    "filepath = os.path.join(filepath, detector_name)\n",
    "if load_pretrained:  # load pre-trained detector\n",
    "    od = fetch_detector(filepath, detector_type, dataset, detector_name)\n",
    "else:  # define model, initialize, train and save outlier detector\n",
    "    \n",
    "    # define encoder and decoder networks\n",
    "    latent_dim = 1024\n",
    "    encoder_net = tf.keras.Sequential(\n",
    "      [\n",
    "          InputLayer(input_shape=(32, 32, 3)),\n",
    "          Conv2D(64, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Conv2D(128, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Conv2D(512, 4, strides=2, padding='same', activation=tf.nn.relu)\n",
    "      ]\n",
    "    )\n",
    "\n",
    "    decoder_net = tf.keras.Sequential(\n",
    "      [\n",
    "          InputLayer(input_shape=(latent_dim,)),\n",
    "          Dense(4*4*128),\n",
    "          Reshape(target_shape=(4, 4, 128)),\n",
    "          Conv2DTranspose(256, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Conv2DTranspose(64, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "          Conv2DTranspose(3, 4, strides=2, padding='same', activation='sigmoid')\n",
    "      ]\n",
    "    )\n",
    "    \n",
    "    # initialize outlier detector\n",
    "    od = OutlierVAE(\n",
    "        threshold=.015,  # threshold for outlier score\n",
    "        encoder_net=encoder_net,  # can also pass VAE model instead\n",
    "        decoder_net=decoder_net,  # of separate encoder and decoder\n",
    "        latent_dim=latent_dim\n",
    "    )\n",
    "    \n",
    "    # train\n",
    "    od.fit(X_train, epochs=50, verbose=False)\n",
    "    \n",
    "    # save the trained outlier detector\n",
    "    save_detector(od, filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check whether the model manages to reconstruct the in-distribution training data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot original and reconstructed instance\n",
    "idx = 8\n",
    "X = X_train[idx].reshape(1, 32, 32, 3)\n",
    "X_recon = od.vae(X)\n",
    "plt.imshow(X.reshape(32, 32, 3)); plt.axis('off'); plt.show()\n",
    "plt.imshow(X_recon.numpy().reshape(32, 32, 3)); plt.axis('off'); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting the threshold\n",
    "\n",
    "Finding good threshold values can be tricky since they are typically not easy to interpret. The `infer_threshold` method helps finding a sensible value. We need to pass a batch of instances `X` and specify what percentage of those we consider to be normal via `threshold_perc`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Current threshold: 0.015\n",
      "New threshold: 0.00969364859163762\n"
     ]
    }
   ],
   "source": [
    "print('Current threshold: {}'.format(od.threshold))\n",
    "od.infer_threshold(X_train, threshold_perc=99, batch_size=128)  # assume 1% of the training data are outliers\n",
    "print('New threshold: {}'.format(od.threshold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create and detect outliers\n",
    "\n",
    "We can create some outliers by applying a random noise mask to the original instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "\n",
    "i = 1\n",
    "\n",
    "# create masked instance\n",
    "x = X_test[i].reshape(1, 32, 32, 3)\n",
    "x_mask, mask = apply_mask(\n",
    "    x,\n",
    "    mask_size=(8,8),\n",
    "    n_masks=1,\n",
    "    channels=[0,1,2],\n",
    "    mask_type='normal',\n",
    "    noise_distr=(0,1),\n",
    "    clip_rng=(0,1)\n",
    ")\n",
    "\n",
    "# predict outliers and reconstructions\n",
    "sample = np.concatenate([x_mask, x])\n",
    "preds = od.predict(sample)\n",
    "x_recon = od.vae(sample).numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Is original outlier? No!\n",
      "Is perturbed outlier? Yes!\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if outlier and visualize outlier scores\n",
    "labels = ['No!', 'Yes!']\n",
    "print(f\"Is original outlier? {labels[preds['data']['is_outlier'][1]]}\")\n",
    "print(f\"Is perturbed outlier? {labels[preds['data']['is_outlier'][0]]}\")\n",
    "plot_feature_outlier_image(preds, sample, x_recon, max_instances=1)"
   ]
  },
  {
   "attachments": {
    "deploy_diagram.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploy the detector\n",
    "\n",
    "For this example we use the open source deployment platform [Seldon Core](https://docs.seldon.io/projects/seldon-core/en/v1.1.0/) and eventing based project [Knative](https://github.com/knative) which allows serverless components\n",
    "to be connected to event streams. The Seldon Core payload logger sends events containing model requests to the Knative broker which can farm these out to serverless components such as the outlier, drift or adversarial detection modules. Further eventing components can be added to feed off events produced by these components to send onwards to, for example, alerting or storage modules. This happens asynchronously.\n",
    "\n",
    "![deploy-diagram.png](attachment:deploy_diagram.png)\n",
    "\n",
    "We already configured a cluster on DigitalOcean with Seldon Core installed. The configuration steps to set everything up from scratch are detailed in [this example notebook](https://docs.seldon.io/projects/seldon-core/en/stable/examples/outlier_cifar10.html).\n",
    "\n",
    "First we get the IP address of the Istio Ingress Gateway. This assumes Istio is installed with a LoadBalancer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "188.166.139.197\n"
     ]
    }
   ],
   "source": [
    "CLUSTER_IPS=!(kubectl -n istio-system get service istio-ingressgateway -o jsonpath='{.status.loadBalancer.ingress[0].ip}')\n",
    "CLUSTER_IP=CLUSTER_IPS[0]\n",
    "print(CLUSTER_IP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vae-outlier.default.example.com\n"
     ]
    }
   ],
   "source": [
    "SERVICE_HOSTNAMES=!(kubectl get ksvc vae-outlier -o jsonpath='{.status.url}' | cut -d \"/\" -f 3)\n",
    "SERVICE_HOSTNAME_VAEOD=SERVICE_HOSTNAMES[0]\n",
    "print(SERVICE_HOSTNAME_VAEOD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We define some utility functions for the prediction of the deployed model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "from typing import Union\n",
    "\n",
    "classes = ('plane', 'car', 'bird', 'cat', 'deer', \n",
    "           'dog', 'frog', 'horse', 'ship', 'truck')\n",
    "\n",
    "def predict(x: np.ndarray) -> Union[str, list]:\n",
    "    \"\"\" Model prediction. \"\"\"\n",
    "    formData = {\n",
    "    'instances': x.tolist()\n",
    "    }\n",
    "    headers = {}\n",
    "    res = requests.post(\n",
    "        'http://'+CLUSTER_IP+'/seldon/default/tfserving-cifar10/v1/models/resnet32/:predict', \n",
    "        json=formData, \n",
    "        headers=headers\n",
    "    )\n",
    "    if res.status_code == 200:\n",
    "        return classes[np.array(res.json()[\"predictions\"])[0].argmax()]\n",
    "    else:\n",
    "        print(\"Failed with \",res.status_code)\n",
    "        return []\n",
    "    \n",
    "    \n",
    "def outlier(x: np.ndarray) -> Union[dict, list]:\n",
    "    \"\"\" Outlier prediction. \"\"\"\n",
    "    formData = {\n",
    "    'instances': x.tolist()\n",
    "    }\n",
    "    headers = {\n",
    "        \"Alibi-Detect-Return-Feature-Score\": \"true\",\n",
    "        \"Alibi-Detect-Return-Instance-Score\": \"true\"\n",
    "    }\n",
    "    headers[\"Host\"] = SERVICE_HOSTNAME_VAEOD\n",
    "    res = requests.post('http://'+CLUSTER_IP+'/', json=formData, headers=headers)\n",
    "    if res.status_code == 200:\n",
    "        od = res.json()\n",
    "        od[\"data\"][\"feature_score\"] = np.array(od[\"data\"][\"feature_score\"])\n",
    "        od[\"data\"][\"instance_score\"] = np.array(od[\"data\"][\"instance_score\"])\n",
    "        return od\n",
    "    else:\n",
    "        print(\"Failed with \",res.status_code)\n",
    "        return []\n",
    "    \n",
    "    \n",
    "def show(x: np.ndarray) -> None:\n",
    "    plt.imshow(x.reshape(32, 32, 3))\n",
    "    plt.axis('off')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a prediction on the original instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'ship'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show(x)\n",
    "predict(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the message dumper for the output of the outlier detector:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlier? No!\n"
     ]
    }
   ],
   "source": [
    "res=!kubectl logs $(kubectl get pod -l serving.knative.dev/configuration=message-dumper -o jsonpath='{.items[0].metadata.name}') user-container\n",
    "data = []\n",
    "for i in range(0,len(res)):\n",
    "    if res[i] == 'Data,':\n",
    "        data.append(res[i+1])\n",
    "j = json.loads(json.loads(data[0]))\n",
    "print(\"Outlier?\",labels[j[\"data\"][\"is_outlier\"]==[1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then make a prediction on the perturbed instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JTECfIDODG9xT0runFuNrXiP9lroN/HsW4B7f6uNn51L/JoxFZLJ1ewP3dqq18Wb6eit8/qtr+POaHZyxR+jNKYRTJE4hnCJxCuEUiVMIp0icQjhF4hTCKdRK2SIbzq9dwTbLfALsiAjbFG++dgnG9nLc+4b9uwwW4fb4gzlum78gm9sNjQowsyTC/WNYP5r//MZzweOfbODp1efIlOdRB1sAizm2gqJ5+LrHU2yZ7ZERA6jowMzs8qu3Yaw/Cm9UH2f4/tY28HO6soU32Vfa+LlKyDiGeifc96lSxxZRlFCpBdGbUwinSJxCOEXiFMIpEqcQTpE4hXCKxCmEU2h+99ipYzC2T3qzDK6hNDpOh4+JhbEzxyMScjK2YAoqTIqSVJeUy41jiEo2URqve/273wwev3of2z3rMe5VU5bY7imIBXMAKnhugdEDZmavk4qga2TkxbCOf7PWsUPB45vHPwDXVLt4bIjF5BFP8P1oNrGVVQcVK3GGK3HK6J2/B/XmFMIpEqcQTpE4hXCKxCmEUyROIZwicQrhFGqltFfwbv/1TTyt+SawUoijQAcoT0hjrRlZhyyTwpacXk0oScUKu/DZKDwiYdDHowLiCq60SCbY+rhB7uOLFrY+Xk/xvRo0cVO2xlE8fmD98GEY662Hxy5UGriCZErufUmssUqKm5clLJaEY0lKppGDNQy9OYVwisQphFMkTiGcInEK4RSJUwinSJxCOIVaKTUyo6RCZmFkYCpwMcNpbVLUYXMyh8SYLYKWsS8jVR2MBSk9KUnsYBE+/1enZKp4jqtSXh3j5lkvz/HU6x3Q7Gr12HG45tAHsSXSJc3hKqR5WbwI36sZsUSSFDfjSkilSJrjdVGMf7OiCFtSEfmdY1WlCPH+QeIUwikSpxBOkTiFcIrEKYRTJE4hnEKtlBlpujUY4fkfrW41eHw8wE2fCmApmJkVJA1dMOcDBCPS34vXzmBKYs+UZE7GIA7f44vTPbjm8pA0Q6vje5Vu4oZtW0fWg8ePr6/BNb0OHrMeE7tkQKpIxsA2S0mVSJXYelUyvyTNw8+pmVm1hqtgKtXwuizDVTrLoDenEE6ROIVwisQphFMkTiGcInEK4ZS3ydbi7GqS44zbyno4QzZr4o3Gc7IpnoRsRrK8JcjWgskDZmYWkWwt29jMNrdbirN4aQo2epPJypMO3lR+ooN7O62s4rEFzXb4UWjWcZa0UsWPz5hM0Z6SXkYlyHgmGXlU2b0nsYxsfGc9hDJwLqi3kNnb9JgC6M0phFMkTiGcInEK4RSJUwinSJxCOEXiFMIp1EpJMpyG7q7ijc1NsPm6mOJ0MrNS5gWxS4j1EYOpxhH5T4pZH5gYp8rjlGw4z/B110DKvtXCG7Y3mx0Ya1Zwf6EG6T2UV8IWxpTs5T4AvaLMzEakaIIVMlSB7ZST4gFmibAxCBGZ9M0mhE+n4anjeY6nkeeZxjEI8b5B4hTCKRKnEE6ROIVwisQphFMkTiGcErGUsRDiB4fenEI4ReIUwikSpxBOkTiFcIrEKYRTJE4hnPI/1/Nt6duWv7sAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'ship'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show(x_mask)\n",
    "predict(x_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Although the prediction is still correct, the instance is clearly an outlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outlier? Yes!\n"
     ]
    }
   ],
   "source": [
    "res=!kubectl logs $(kubectl get pod -l serving.knative.dev/configuration=message-dumper -o jsonpath='{.items[0].metadata.name}') user-container\n",
    "data= []\n",
    "for i in range(0,len(res)):\n",
    "    if res[i] == 'Data,':\n",
    "        data.append(res[i+1])\n",
    "j = json.loads(json.loads(data[1]))\n",
    "print(\"Outlier?\",labels[j[\"data\"][\"is_outlier\"]==[1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preds = outlier(x_mask)\n",
    "plot_feature_outlier_image(preds, x_mask, X_recon=None)"
   ]
  },
  {
   "attachments": {
    "adversarialae.png": {
     "image/png": 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E10oLaWV1RbZuy+aVS3jk2mEUFdqY8Oi/zl6PwYi5eN0RH19fmrdqU/p37N20jinPvV7jWEX1mYOC0JtM1Z4R1RhoeswEIKsuCiGqpM/FI+lz8Uhvh3Feotq057lZP9ZKXROfeK5W6hHnR282V2vtnsZC2y0T2u2BEUIIIUQxTScTqCo6aZkQQgghNE3byQRIN4cQQgihcZpOJqSbQwghhNA+TScT0s0hhBBCaJ/mkwnp5hBCCCG0TdvJBEgyIYQQQmicppMJGTMhhBBCaJ+mkwkZMyGEEEJon7aTCZBuDiGEEELjNL2ctnRzCCG86eD2zaz8aX6lzw+6fBS9Lhp21jr2bl7Pmt9+qPT5kTdMIKZ7r/OOUQgt0HQyId0cQghvOnH4IAu/+qTS5235eedMJv78+Vv++PrzSp/v1m+QJBOiwdN2N4dMDRVCaNi+LetrpYwQDZ22kwmQZEIIoVmpJ46RlZpS6fO2/DyOH9pfjxEJ4R2aTiZkzIQQQmuMJnOZ+2dreTiwbROqolS6rRCNhaaTCRkzIYTQmvZde5S5f7ZkYt+WDWW37dazTmISwtu0nUyAdHMIITSlfbeemMw+pffPTBhOd3qi4RcYRKsOneo0NiG8RdPJhHRzCCG0xmg0lZl9cXTvLgpttnLlFLebgzu2lN7vEte/XuITwhs0nUxIN4cQQmsURSG274DS+263i4M7Npcrd3T/bgpt1tL7sXH9UU4bPyFEY6L5ZEK6OYQQWqKqKl3iBpR5rKJxE2c+FttnYJ3GJYQ3aTuZAEkmhBCa07XPGcnE5gqSic2nxlLo9Ho69+5X53EJ4S2aTiZkzIQQQotCm0fRPLp16f0zp4AC7N96Kplo0ykWv4DAeotPiPqm6WRCxkwIIbTq9K4OW0E+CQf2lt7PSE4kIzmx9H7sGd0iQjQ22k4mQLo5hGhC0hYtwlVQ4O0wqiS2T9nZGaePkSg3XqKvjJcQjZumkwnp5hCiaVGKiljevj3xr7yi+aTizAGVp4+b2L9l4xllpWVCNG6aTiakm0OIpiXquuuwtGzJvqefZlnr1ppOKtp364mPr2/p/cpaJoJCm9GyXUy9xiZEfdN8MiHdHEI0ITodXZ5/HgBnTg77n3lGs0mFwWCkY8+40vvpSSfJTEmi0Gbj6P7dpY936SOLVYnGz+jtAM5JkgnRRBSlpfHXQOlbB9CbTChuN6qi4MzNZd/TTxP/2mtEX3ML+vC4c1dQT2LjBrBn49+l9/dtWU9QWDiK212mjFb42K08+emT2L6x8I2PydvhnJUpMBDfqCiaDxxIhxtuIKxHj3NvJLxG08mEjJkQTYnqcmFLSCDissvw79R0r+GgOp2c/OorcLkAzxoNqqLQbNgwcuIGopx0eTnCU7qcMRZi7+b1BDcLL/OYlharUgwGDrXtRmRMFNERwd4O56xcNhvWkyfZ+eabbH3hBWJuvJGhM2diDgrydmiiAppOJmTMhGiKWt9+O9G33OLtMLzm8Ouv4y4s9NzR6Yi85hq6PPccQb168f1P6+HkPu8GeJozB1bu27ye4PCI0vsGg5FOF/Sp77Aq5TRb+PGySYy4aQhD+zeMhNVls7H/s8/Y+PTT5A4fzrVr12I8bayK0AZtj5kA6eYQoglxW60cfv110OmIGj2aYdu20X/BAoJ69Tr3xl4QHBZOVJv2pfcTDuxh/2lXEW3XtUeZQZqi+ox+fvSYOpWrly4la88eNj/7rLdDEhXQdMuEdHOcncuaSMHxX7EmLcdlTcRlT/V2SPXO4BOK0a8lvlFDCGx7HebgLt4OSdRAwkcfETpkCF2mT9dsAnGmrn0HknL8KOC5UqjdemqgaFcNdXE0dJEXXkj3++5j38yZ9Pn3vzEHa7ubpqnRdDIh3RwVc1lPkrH1OXIPzUZVtNN/7A3OfIAtFBz/lfSNTxLQZhQR/V7GJ6ynt0MT5yF6/HhiHnvM22FUS5e4/qxc8E3Fz8lMjlrVedIkdr3zDsmrV9P2mmu8HY44jeaTCenmKMt6YhGJK8ejOHIBMFvMBAQFYLaYMZqN6Ghax8vlcuEocmDNtVJoK6Tg+G9YTy4m8sJ3Cel6r7fDE9VkiY72dgjVdrbZGloafNkYhPXsidHXl8zt2yWZ0BhtJxOijLz4uST9OQlUN2YfM82jIwgIDvB2WJoQERWO3WonLTEdu9VOytr7cFoTiej3ordDE41c2y7dsPj5U2izlnn8zIuBiZrTGQxYwsMpzMz0dijiDJoegKlKy0Qpe9oGktfcBaqbgCB/2nVpQ0CQv6f1Rm6gqvj6WWjTsRWhESEAZG5/ibz4uV5+5URtMug8X1mKop3xVHqDgc69+pZ7/MzLlGuBu/jKpka9pr/6z8ro74/Laj13QVGvtN0yIWMmPFQ3KavvRHUX4uvvS3S7luj0VTsu+xMcJKW7uLS/Xx0HWTMnUl2kZ7vpE+tTo3p0Oh2R0c1R3Aq5WXmk/H0//q2uwGAJP/fGQvMiAi0A5NqL6mV/I8bdwohx556m+8KcX6pU3yNvzeSRt2bWNKzzlmt3ABARKDNMRO3SfnoqyQS5h76kKGcvOr2O6LZRnkNSxbP1G59OZsR9J9m0x37Wcmu327hg/DGvtSrMX5LHfa+m1lp9Ua2aYzKbUBy5ZO74j7dfQlFLWgR5kuLUPDkzra5Cp4t8u2f9jqggSSZE7dJ0y4RMDfXIOfg5AKHNgjEaDZ4fzCrYddjBniNF9O/mw9d/5NO/q+es//e1VnYccvDM7aH8uLKAo0kuth4o4sAxBx//kMPk0cG89lU2Kzbb6dDSyAv3NCPQT8+Ln2ex+7CDS/r58tDNISzfZGftTjupWW6SM1w8f3czurYz8dpXOfy1w07vTj48f3cYer2uXH3NQw04nCrPfZLFgeNOgvz1oIK9UCm3nz/W2dgZX8TxFBeTRwfRq9O5Wy90QHjzUJJPppF7cDbNB7wGOsN5vwZCGwa1jwQgOSuP9DwrEUH+Xo6o4dh1PBVFUQnz86FTc5lWKWqXtlsmpJsDd2EG9tR1AASFBFZr2zmL8rm4ty93jw7mm6X5uD3dpeyKd7Dwb8+Z3dYDRSzdaGNAdx9MRh0XXeDLh9/n8tEPudx/fTDHU13cPiOVFz/PYvF6G7deGciMT7P4YWUB+xIcvDwrm0E9LGTnKTzzYSYf/5jHB9/lMvEfQXy1KJ//LcirsD6Ad77JYfbv+Vw92I+/d9oBKtzP7iMOpn2UhcsNwQFVf8sGhgSg0+twF2WWHkPRsLVrFkhslGdMzF8Hjns5moal5Hhd3q11gx4zIbRJ+++oJp5MFOXsA9WNwaDHYjFXuZlfVVTmLcknItRAvtVNSqabFZtsp1o1VIrLeu52b2/GaIALYkz8udXG2OH+jL7YjwduDGblZjt/7SgkNcvNW1/noKjw5xZPt0n39mZuuzKAEf19yc5z89d2O/+4yI+bR/pz5Mc23DMmsML6UFX+3lnIdRf7ceeoQCZf51lvv7L99Opk5uMnw2kXZazyMdDrdFgsnlaMouxdXnj1RF2488JYAP7Yfoi8eho70dAdSslke0IyAHcMkoXdRO3TdjIh3Ry4bEkAGE3V65Favd1OYrqLPKvC4g12WoYb+XpxPgAWs47MXDeqCvuPOUq3KTncLcON7Etwoqqw54iD6AgjESF6xgzz56+ZLfni2eZMHu358TcZyyZ7oUF6EpKdADzzURa/rLFVWB9AWJCeQyc8ZQ8c8/xb2X4s5vNLKkuOm8uWfF7bC+2ZNKgL7ZsFUuh0MXPZZk3N7NAia6GDmcs2A/CP7m0Y0K65lyMSjZGmkwmZGgqK07M0r16vq9YAxLl/5DP4Agt/vB3FH29H8dTEYH5cZaWwSOGqC31JzXLT7PKjHD7pBFWlfQsDTpfKlFfSeezWYI4mOYm8KoEZn2Xz9kPNeGZSCN+vLCB61DGeeD+TAEvJ63LafoEHbghm9xEHISOP8usaK4O6+1RYH6rKo+OD2RnvIPyKBLYfKgLUivdT8ltxHgMxDcWzXkqOo2j4zAY979wwGKNez64TqXzx59bSKY+iLGuRk3cWrSMtz0ozfwv/uU4W0RJ1Q6dqeJTjD717E9azJ5d89ZW3Q/GanAOfkbLmLnz9fGjbvmWVtzuR6iIjt/IvWLcbipwqfhZdmcdcbhUfsw5VhbRsN2FB+tLWB5dbJTNXISLUwNlmprrdkJHrpnmooTQXrKi+kjrtRSqBfvoyj1W2n14dzVSnuzclKYOc7HzCejxM80FvVX1DLyhMSmJpdDR95s5t0lcNrapP1+7j8QXrAegaHcFdl/QlPEjbU6Dr05HULP63bDMpuQWYDHoW3H0Fg2OivB1WjX3btStRQ4Zw8SefeDuUCn0eEECvxx+n7/Tp3g6lXml6NgfQ5FsmztemfUVs3Nv4+pO7tQ/Fp4prbIjG7a7BXTEZDTzx4zr2Jabz1LylDOvWjgs7tSImMqxJDt52uN3sT0xn9b5jbD6SiKpCmJ8PsyZe0igSCaFdmk4mNNxoUv9KBkxW0dhhfowd1kjP0qrzvpD3UKM2aWBnYiNDePzHdexKymLZrsMs23UYX7OJsAALwb6WJpFUuFWFXFsRGXk2nG536eMjYqN5Y8yFtGtWvZlgQlSXppMJmRoqhDiXge2as+rha/lx+1HmbjzE30dSsDucJGY5SSTf2+HVuwAfEyO6RHPX4K4MkdYIUU+0nUyAdHOcTs6yhaiQXqfj+rgOXB/XgbxCB7uTskjOtZFWYKfI1fgHZ/qZjEQF+dIy2J+e0WH4GGWBNlG/tJ1MyI/naVQ5HkJUQZDFzEUd5IxciPokU0OFEEIIUSOab5mQMROnkZaJ6vPyMVNVhZS8E+QX5uBSXGct68zJJmdIB46a0slN3nLOuo16I4GWUKKCWsvnRAjhVdpOJkBaJk4nuUSDsidpE8v2/kBeYXbVN3pyJAnshS17q7xJsG8Yl3W7ga4t+pxHlEIIUXOaTiZkauiZ5Hg0FJsTVrFo9zwAdDo9vmZ/dNRuYqyiYndYybVn8f2WmYy6YAJxbYbU6j6EEKIqNJ1MSDfHaaq5zoQoUf/HLL8wh6X7vgegVVgnLmg7BJPh3JdNPx8OVxE7j68hMSuexXu+pXNkL/x9ZE0BIUT90vQATEC6OUSDsy95Cy63kwCfYHq3G4ZBb0RR3XVyMxqMxLUbhp9PIE53EftTtnn7zxdCNEGab5kQp5HjUX1eOGSZ1jQAQgOjUHDXSwxhAVHYivLJsqbW/c6EEOIMmk4mZGro6ep2nQlbkcqT/8tHreCX79kJgUSFVa0RKyNX4bnZtbPq4EXdzdwywrdW6qpPiuJZzliv06GoZ5/BUVtKugPdivscJYVoWJJWrEBxOml1xRXlnlOcTg7OmkWryy8noG1bL0QHCQsW4BsVReSFF5Z7zmWzsf+TT+g0cSI+oaFeiK7+aDqZkDET9WfBmkLe/8la4XMxLY08fL1/lerJKVD44CdbrcRU6KBBJhMlVFQUtWY/7qqqUmgrBMDH4oPeUFlS1/hXeRRNU8SAAcxr356gmBj6Pf88AKqisP+TT9j60kuEdO5M7OTJ3ouvf3++6diRqIsvLo1PcTrZ+eab7Hj1VdqMGkWPRp5IgNaTCZCWidPVYcvE3GX2sz738LgqXjSsNkNUa6E1xotdQ6qq4K5hy0ROZi5Xx40D4P1v3yJu0AUVllOkC0w0UqaAAC547DE2PvUUC6+8EoPFgvXECQ58/jkAl86d69X4/Fu1Ivauu9jzwQckLl2KzmBg19tv47LZ0BsMxE2b5tX46oumB2DK1ND6kZ6jsHRL5Zcr33LQyYET9dNc35iUtEzU5BYQ4o+++JLrwWGBlZaTlgnRmHW///7SbgLF6cRpt4NOR/SIEUQNHuzl6KD3U0+hN5k8d1QVt91zctZp4kSCYmK8GFn90XQy0RS6ORLnziX+1Vdx26rQNVBypl7Lt29W2nGdozX+6+X2qtdZAX+Lrto3i1lX87+vDhx68UVOfvklqvvsB03F0zJRk5uqcxMY4pnqGRQWUGk5aZkQjZkpeI/OeAAAIABJREFUIIBeTzwBOp3nc6cooKr0nT7d26EBntaJrnffjc5gQFUUVFVFp9fT++mnvR1avZFuDi+LGjuWFTExHH7zTTo9/TRt77kHg28F4wTqcJ2JucsKz1nm6+WFPD+xKuMmKo4xYU4zwoPPI3fV4I9ki3HjWNWjB4deeYUuzz9Pi3Hj0OnL/22qWvGYiW1/72TJjysA6NGvK1ff7BlY9ufCtaxfsQmAkWMuoe/gXgCEhAWTn1tAQLBfpWMwKho4K0Rj0v2f/2TH669TlJ2NTq+n5fDhRA0d6u2wSvV+6in2zZyJ6najMxjoPHEiwZ06eTuseqP5lonGzuDrS8wTT+BIT2fPI4+wrE0bjrz9dmkzWV07nORmw35nmccigvXlfvjjk9xsPKNcUxXQtSstb7yRgn372HLjjazq3p2k775DVcp2NagoKKqr3K1rn07s3XaAxT+s4L3pM0k8lkhebi5vP/shi39YwZ6t++nWp1Np+aDQQIJCAlF17grr88wYkW4O0biZAgLo9fjjoKqobrdmWiVKlLROAKCqTapVAjTeMuGNqaH5e/dSlFq/c/UDYmMxhYTgzM3FmZnJnkce4dDLL9PpmWcIudRyqmAdJFdzKmiVGDPYjMsNny8u+9zc5YUM6HKOt0xlMdZht8NZFe/TfuIEGfaVtVZtxBVXkDR/PqqqYj10iC033khAbCxdnn8euqjFu1ZxV9CSYDDreeTVe3nkpn/hdDj56KUvaNE2krzsfHR6HY+9fj9GH0Pptrc/ejO2AnuFdZWQbg7RFJS0ToTHxWmqVaJE76eeYt+nn9Lp1lubVKsEaDyZ8MaYiUMvvEDiN9/U6z5L6XSlg04dGRnseeQRAnvH0PxOoG5WY+brleWTiesv9qHIWT6ZmP9nEW/dE0ClsxM1LPn770n/9vvar1ivLx07UbB/P1tuuons/06GdrrSlomKxMbFMHrSVSz4YiF/L92IwWAAYOydV9Old4cy23Xr5/lSOvuaFdIyIbRPVRzYklZSmLkNxZFzXnX0e64n/q2iSN/0VC1HVzv6Te9BeF/jecenM1gwBbTDv/VVGH0jazm6uqPtZAK8MmYiIDaWPnPm1Nv+3DYbG0aNwpWXh06vR1UUfFu3pvP06QQMcpO2bgp1sWjVpoMuDp4se7YbFqjjkgtMuBWVAF8dBfZT+0zNVli2pYgr+pkrr1RrLRPFom+5ha5PPFJr9VkPHmTLLbeAongGXbndhPTvT5cZM9jUMoNjx9d4kgkqTwAmPjqODSu3kpSQgtvtplVMSyY8PPas21RGlWRCaJz15GJS1t6DMz+hRvUYfKEw03PTIksEFBzfQsHxmtWj05sJ6/EQ4f1eRKc31U5wdUjTyYS3poYa/PwI7tu33vYX/9pruPLyALBER9N5+nRaTZyI3mQi58BnpwrW8vGoqIvjugvNGPUqRj1c1c/Ed2scZZ7/emURV/Q9yxu7khgf/tiKTzU+D9deaObaQWdJWqqqOB6fyMhafU0PvfRS6f+D+/Shy4wZNL/ySs8DO+cU77ribo4SerOeZpEhJCWkeOoJDUBv0p11m8rINGqhZflHfyBxxU2gutHr9fgG+KKvYNCy8Ci0FeJ0OMjc+RqOvENEj/gedNo+XppOJprC1FBXQQGH33gD3zZt6Pzvf5cmEXXNrcD81Y5yj18/9NQP+JjB5nLJxIK1Dj5+QMXXXL3XZc6KytexqEirCH3tJBN1IG/HDpJ/+qm0JaI0iTjD2bo5ABbOXcmuDftL7+/ZfJBfv1zCqIkjqh2TzOYQWuUuzCB59Z2guvEP8qdlmygMRoO3w9I2FbIzc0g9mU5+wgJyDnxGSKz3VvmsCm0nE9Dop4amLVpE1//859xJRC1PDV221Ulqdtmm8WB/HSN7m0r3c3V/E2YjOE77Pcy3q/yyzsFNF1fyQ19bMWr4kuvpS5YwcOHCSpOIEiqVt0yknshg9mueMRw9BnbBVmDnyJ7jfPXmD8QN605Um4hqxaSq0s0htCnn4OcozjxMZhPRbVt4FmHT6GdbS0KbBeMqcpKZnk327nckmaiRJvCGa3nDDVUvXIvHo6KWgmsHmjAb1NKlIoJ84dJeJv7YUnZK6Ncri7hpaCWJT62F6N0xFmcT8/jjVSqnqhW3TKiqyvtPz6LIXoTeoOeOaddjy7fzr1vfosju4INnZvHclw9Vq1VOWiaEVtlT1gAQHBKAHhUUea9WVUhYIJnp2RTl7MVdlInBp5m3Q6qUppMJuWpo3bAVqfy0rvyaEdcPKd/aMOai8snEos1OsvJVwgKr/trEtjZUaxZI8/NZ4EpjKrvQ16Kv/mTvpkMAjLzxIlp18ozYvvCqONYt2sbeTfEs/GolV902rFr7EkKL3IUZABhNRs2eIGiV8bTuILc9XZKJ89YExkxUXe2dqf+8zlFmlgZAoK+OK/qU/7CPHmTi3vfLnkw4XfDdGgdTrqqgq6OSGNe8FkB4UDVfy9r4ezV4oa/LJwzm8gmnridQUmbqm7cx9c3byj1e1X0JoU3Fn0Evz+hqiHSnHS6tz9jSdjIh6sScleVbJSxmmPRWxZcg9/XRYS0s+yXw9apKkglRqjYuQV6dfQnvyU38k5wTKyiyJkE9vebVotNj9m9BcMuhhLa5DPDSSZokE9XTgA6XppMJ6eY4Qy18EDPyVJZsLZ9MpOeqzF9d9eWy1+xxcTzNTZuIM7sjzrbORDUCbQQqGzNRV/sS3pER/z3ph77zdhjnZHfkY88+iMOaTGTXSfUfgKJ4bmeYvcjKuj2eMVx3Xh3AgK5nP0mZt8zGz395Lox4/XA/rh/uV6vlNaUBfWdqOpmQbo4z1EIyMX+145xXCK1qKPNWOXjyep/yT1S2gTc+GF44EzIbPcfE7XZXq6uiJlyKq3jflnOUFLXJ7cgnI/5HAMJa9CIgtL1mT4CsOSfITNxM1rFFhLW7CpNv8/oPooLP48qthcz+w9MqOqSnDwNizz41fke8g/nLPclBbBsj1w+r4MKINSivKZJM1CKNfjAbqoq6OM7X3D+d5ZMJQcuQ9gDY7TacroDSpbLritvlotDmObOLDmlXp/sSZRXmJ6CqbowmPyLbDcJr3QdV4B8USX5WPA57DvbcI/WfTFQ6ZkKtQpkz6jl909ouryUNKFRtJxMN6UWva7XwITiSorD+QPlmibgOeoL8zv4lmJWvsutY2SbKXQkKuxPc9Gh7WleH1lomvCA2qjehfhFk29JJT8kgIMgPo7luPmoup4uCXBuqqhAeEEWnyJ51sh9RMcXlWUVWrzeCov2r6ur1nveh6i6/+m2JwqQkLC1b1v7Oq/QdVt1BmnVd3ssaUKiaTiZkzETtmvtn+SZ3XzOsfsWPAMvZj3Nytkr07QXlPodz/3Tyn4nnbp147PMiLOcxXvPxsWZiohrWNFGD3sgN/e5h7oZ3sBblk5uVX+f7DPAJYlzfKeh1srKgd6igelaLvXny6yQcL3vl4StH9OW5J8aftYafF23gpbe+ZePSN8/r+aqpfGyN9dAhDr74Ir7R0cS+/HIN9lEJteIxE2W+VBS1tEyuVeWt7wpwF2/SO8bE9cMsZ7Q0qBXXWVn9FZTfuN/JBz9ZWbfXSWauQqCfjl4xJiZd4cvYoeW7Da2FKv9dYOX39UWcSHPjb9HRJtLAmCEWJlzmi59P+e/S89mmodF0MgHImInT1TCjnruq/JnT1f2MBPicu+4WITCws6Fcy8a81U5enmA+lfNVUs/sFed31jZhmJGYyIb3HogMasXkoc/y9+HFHErdSV5hDm6ldsdPGPRGgiyhdI68gMEdr8TfJ6hW6xfVoYLiSSauuawP2bkFvPLeAtpEh3PLuKF0jmmJ21nouWS9rYjgID9UVSU5NZuw0EAsPiZiYyKZMnFkaT0ALpeb9Mw8opqHnPP5ku9KVVVJz8wjolkQbrdSZq2Cik51S5KIxDlz0FssjExIqJMjVBxcBY+dcUdVcbpg3PQslm/1/K0dow08MMavfDdIDbs53v7eyqMf55cpkpUPx1Ld/PJ3IRNG+jLryeDSNXIy8xQufiiLvcfKfpb3HnPxx8Yi3phfwF/vNaN5yKkToPPZpiHSdjLRkJqj6lzNmuc2xyscSCyfwd80xFDlescMKp9MHEtTWbvPxZCuxV9Ytf6a1bBZ0ovvoUBLCFd0v4krut/ktRhEfTnVMnHruIEAfDpnGd06t+Sfd47g49nLuOuhDwkK9GX0Vf0YP/YirrjpFTKz8tHr9Xz5/r3kFxTy4LQv+L9bhjLrm9U8On0OLaNC2b3/BMMv6sZtNwzhwWmzK31++Q/TSMvIZfiYFzgQn8zAPh3ZsDWeQ+vfpmP7yFNxFjs9iVCLf6Tb3X8/5ojqLeVe9UNUlfEQnnJT3s4rTSRahOlZ8moozYMrWob7/Ls5/tzpKJNIdGhhoF9nE/FJbrYe8pz8zFlmp2d7A0/c5A/AM5/mlyYFQ3qYGDvUQq5V4ZPf7SRlKhxKdDN9Vj4fPXgqsT+fbRoiTScT0s1Reyrq4vC3eFomqmrMQANPzq647tJkQoimSjnzwnmK56Y4QHVzIimTj16dxDWX92bz9qPcefMQHrv3Ki6/+Q2+WbCWq0f2OlWP6iIrp4A1Pz/DgcPJjL3zvwy7sPNZnz9+IpmZc1ZRUFDI8S1v8fWP69iwNd5TviS24l/OY59+yvEZ80CvRy1u9tcZjbiystg9dWqtHhZjl+NgoWoDMFGZ8VUBX/xhByAkQMfiV0JoH6k/tW0ttUz864tT3bZjh/jwzbRgTMVfhzPmWJk+2zPD5OWvrdx3jS8Bvjp+W3/qMgSznwiiQwvP9941g3wY9kg2AH/tcpbZ5/ls0xBpOpmQqaFnOM83m1uBb9aUTyau6WfA11T1gZGdWujo3kbPnuNlWzi+W+vmvbtUTAZqf8BQQxt9LZomVQW1qILH3J7HVRchQX7cc9tFABxrZuHbXzbw3a8bKXK4iIoIALW4K1AtAtVJWIg/3To1w+HwTGssLLSf43krh44kEdezNa1b+HP1iG489RKA47TYPJ/d/F27PElEyWdL5znrT12ypNa/cyPvzsVUkkycY8zENysK+X2jJ/HxMen49fkgerbVl92uFsZMpOcqrNl1quv1jcl+mPRK6ZCSp2/05aNf7KRkK+RaVZZvLeK6C82YjaeOzdXTcrj1Uh8u6WWiX2cjBT+fttT1aTGdzzYNkbaTCZCWidOd54+qQQfJn1ey/kA169z9TmWDLT1JSccoUH+s5XncNUomJBER9UGtoGVC5VTLhAuDQVda5q2Pl9C1UyQLPp3MsHHvesqq7lP1qG7PV5/iODVLRFXO/rzipFO7cD6d9zc79xzl+9+3lz5+ZstEt9dfJ3voPuJffpmitDTPU243XaZPp83//V+tHpljvwzCnrahzP4rU5JIADhdKgElSUilzq+bY99p4xfCAnVlWz4AkwF6tjeQUnxl5b3HXFw3yMRNw8y8+q0nqdt/ws2/ZnsSOR+Tjiv7mXjgOguX9i67Tsb5bNMQaXvEh5yRCiEahOIxE6ffUD0JgOpJJjzFPM8NHdCORSv2EjvsBWz2IpJSsoHTypz+/5IWC9xnf1518sjkIXTt2Jz+/3idjduOAqDDVTYmQG820+HBBxmRkED3d97Bp3icxMEZM1CcdTTFtaSbo9yt4uKKCv/8wFrxNqV1nq3eyssXOk49FuCrq3CbQN9TJ7KFRZ7HXrjNl3/f4kuIf9mT3CKnys/rHIx4Ko/XvrWXqed8tqkwdo3TdDIhYyZOU9UPjdyq9EUlRO1ST41NKL5tXzSVT18dDYqDe27tR8b2Z0ufe/COgRxb9yQ7Fz/Apl/vY+Mv93H7uF4U7HseFAe3j+tVWr531wjUY//hlScvO+vzsR1COBCfxLirulOw/3kmjOmFwaAnItR82riJsh8Ig69vaVLR4913UV0uTs6uYGBUrRyiyn4sy/6wf/6wPyUTUNbudfHV8qKz/LhWI5E4rXxUyKnfleQsBaez/HbHUk8NNm8R5kk4TAaV5ydYSPk6hMUvBvD0jRYGdDGiP+1natpsG5m5Smk957NNQ0wmNN/NIWMmhBDap1Iym6OqIpuZATe1eWGw8FATn8zbwBMvL8JsMvDiY5cS5K87LbaK++UNvr60f+AB2t59N2lLltRaPGWoaiVjJk7995VJFu4YYWTLITMf/OaJ+YnPbIweaDjVUlCmpaGSOs/c7xnlu7fWERaoIyvfMw31m1VF3Hbpqa6GPcfdbD186nUZ2k3PjsNOpn3pWeyrdbiej+735fLeBsCHo6kK/R4qICtfxeWGAydcXNTVwI6j7mpv01BpO5loQFlZvZDjIYQ2qWoFYybqX5d2QexaNBmnS8FkLG54Pj2uc3yH6C0Woq69tu4CrHD/px4L9vOUeW68D3NWOsm1qqRkqzz/dSFv3FnBuK+SFtsq799T3qCDu68w88r3noGp931oJ8+mcFFXA4eTFR77vKi02mE9DHRvredkhsLCzS5KGsz7dTJw6zATFjNY7WqZnKZ1uGc/zQKo9jYNlaaTCenmAJ3O84WgNrRlYDVCLTlmTfx9JOqG3uAZkKyUjI3QiMpmVikl00ANXrggXGXN9uoZd1SV8EB45gYzT87y/Ni/94uD/xtpomtr/Rl1VOF7sZLyz95o4pcNTvaeUCgoVPnnx+WXGA/x1/HxfZ5VN1s10zHpUhOzljtRVbjrPTuT/2vHYgL7aS/9DYONtG6mO+9tGipNJxOoMjXU4OOZOuRu2LOGvMZd/Nk0WMK9G4holCxB7dDpDLhcblLTMgjw1+6F72w2Bw6HE9DhG9S+/gOodAyAWva/xWUeHGXio4VOEtIUnG54YGYhS2f4lt/0PFomAPx9YNVLvtzzUREL1rvKVTOgk4FP/ulDbPSpH/mP7/XB3wc+XeqkqHhpiJKkwGiA24abeH+KT5mYzmebhkinqtr9C2aFhNDjgQfoN2NGve1z6/jxFBw8yMVbttTbPs+mMGMzCT/1R6eDzoEmOcGupqP5LooUlRYXf0Fw59u9HY5ohNIPzifj8I/eDqPKQttcTlT32p3+eTYlU0OjAsyEWLR5/pqSo7L+gEJWgUqARUevdjq6RFc+PyHHqrI5XiE52/Pz2TxYR7+OepoFVv4FfT7bAOzP8EwlbX/9HnxCulX3T6s32nxlS2g3z6k3PmG9MJhDcDtysLpVAoySTVSVU1EpUjzvId+oIV6ORjRWEZ1vxOQbQc7JFTisKZztYlreZPKLJLjlUMLaXuWdAKrbilCPooJh9IAzkoezxBriByMvqCDZqOVtGhJNJxMyZgJ0ehN+rS4n/8i35BS5CTA03NG+9S2nyPOl7hPSDXNQRy9HIxovHSGtLyWk9aXeDkTjZNxXY6bpZAJkaihAs56Pkn/kOwpcKlangr+0TpyTQ4EspyeZCLvgMS9HI0RTVnpJYUkmakCHtr/3tZ1MyBsPAEvEAAI73ED+kW9JKlRo62fArOnlxrzLrUKi3Y2qgk9YT4I7TfR2SEI0WSWDyF0uBeREqFqcyqnfQIOljq7mWks0nUxIN8cpUYM/pihzO47cgxyzuYm26PGTHo9yHAqcLFRwKGAwhxA94jvQyYESwlt8o4ZQcOJ3cp0qzXxUjZ9fa0uOw9O6ag7uovkZado+v5WpoaUMPqG0uvw3TIEdcKtw3K6QXKhSpM2xXvXOqUCaQ+Wo/bRE4rIFmIO7eDs0IZq0kM53ojcF4lRUEm1u3EptL5nfOG85DoXM4i/4sB4PeflVPDdNt0yIsszBnWh33QaSVk3AenIxuS6VXJeKSQdmg+fFbGqpl0sFh+ppkSjhE3YB0SO+wxzc2XuBCSEAMPg2J2roJyStvIUCl8Jhqxs/g056PCqhqFCoqKXfaQFtRhESe7d3g6oCbScT0s1RjsESTusr/8B6YhEZ21/CnrYOp6rgdJ1728bOJ7Q7YT0fI7jTbdK1IYSGBHW4Cb3Rj5S19+KyJlLgkvFw56LTmwjpdh/NB7wGOm13IoDGkwkZM1E5/9ZX4d/6Klz2VGxJK3HZEnEXpns7rHqnNwdj9IvGL2oIpsAO3g5HCFGJgDbXEBN9GdaTiynM3I7qtns7JACOvv8+gd26EX6pRqb26gyYAtrg3/ofmPxbezuaKtN0MiHOzegbSVDMzd4OQwghzklnsBDQ9joC2l7n7VBKbVsyG//woUT0f8XboTRo2m47kQGYQgghhOZpOpmQbg4hhBBC+zSdTEjLhBBCCKF92k4mhBBCCKF52k4mpJtDCCGE0DxNJxMyZkIIIYTQPk0nE0IIIYTQPm0nEzIAUwghhNA8TScT0s0hhBBCaJ+mkwkhhBBCaJ+2kwnp5hBCCCE0T9vJBEg3hxBCCKFx2k0m1OJL1EoyIYQQQmiaZpMJVZXr3QshhBANgeYvQS5jJoRofOwFhRzbm0Ti4VQKcmzYCwpxuxRvh9Vg6Q16/AItBIT40aJDc9p1bYlfkK+3wxJNiHaTCenmEKLRycssYOMfO4nfflxaH2tZTloeAAe3JPCnDjr0bM3Aqy4gJCLIy5GJpkCzyYR80QjRuOxYfYD1v29HcXtaIHwDLTRv1YyAYD/MFrOcN9SAqoKj0IE1z07aiUxs+XaO7DxBwu6T9Lu8J31HdAc5vqIOaTaZKCHdHEI0bKqqsuq7jezfeASA4PBAuvSLIaJlqJcja5y6D+pEZnIO+7ccISctl41/7CQrJZcRt1yIXi/fp6JuaHYApnRzCNE4bFy0szSRiLmgDYOv6SuJRB1r1iKEwVf3oXNce9BB/PZj/P3LVm+HJRox7bZMSDIhRIOXsCeRrSv3AhDbL4aYC9rUSr32Qjt/LF1Yel9v0NO6ZWv69O5XK/VXR2ZWBouWLmRA34F07til3vdfKR10imuH2WJi97qD7PrrIJFtw+kU19bbkYlGSLPJhIyZEKJhU9wK637bBiq07BBZa4kEQE5uDg899c9yjw/qfyFffDQHP1+/Srd1u92MvG4YY0ddz9R7Hqr2vs/c/viJY0yb8SQzpr2krWSiWNuu0eRlFXD8QBLrf99Oh56tMBgN3g5LNDLa7eYoJmMmhGiYDm5NICc9H6PRQLeBHetkHzeOuZnNf+5g5W9/MXH8HazftI4PZr4HgKIobNu5laUrF5OZlVm6TUpqMkeOHubo8SPk5OZUWq7EgUP7WbVmBYlJJyvcvlNMZ+bP+oErRvyjdJt9B/awcMlv7Nqzs0xd9kI7Bw7tp6Agn917d7H67z+x2qx1cWjKiO0Xg8lspCDHxr4Nh+t8f6Lp0WzLhHRzCNGwHd5xHIDWXVrg42uuk31YLL5EhDcnIrw5zz4+ne8WfMOyVUuYOuUhJky+iU1bNwLga/Hlw7dmcumwkdwwaQwAP/z8HQaDkaMJhyssp6oqUx+/l18X/QyAXq/nmcf+zRdzPi3d3t8vgLHXjOOm28cxY9pLTLrlTh5+eio//vJ9aYxXjLiSj97+FIPBwO69u7j+tuvo3TOO7bu2AdAyqiWLf1pJUGAQDzxxPytWL2Pr6l2YzbV3zEw+Rtp1i+bQ9mPEbz9Oj8Gda61uIUDDLRPSzSFEw+V0uEiMTwUgsm1EvezTx8eHyOZRpKal8OW8L9i0dSOzP57L9rV7ad2qDdNmPAnA8l9XAzDhpom0b9u+0nJLVy7m10U/c/cd97J+xVZ6dO3JOx++ydKfVpZuP/3pGWViWLz8D3785XumTnmQA1uO8tjUJz2P/fp9mXKBgUFsXbObybffQ1JKEhs2rwOgTavWdI/tXictspFtmwOQciyDQltRrdcvmjbNJhMlpJtDiIYnP8uK26Wg0+sIjQyut/1abVaCg0LYtnMrer2e2fO+4JFnHqDAWkBSShJp6amYTCYA9HoDu/bsrLTc9p2eloPbbp5Ei8gWLJj3G1tX78LHYind3mgo27i7YfPfAEwcfwcWi4U7b7sLgI2b15cpd+XIf9AsrBmD+l8IQEFBAQCPPfAU82f9WBpjbQoOC8BoNKAqKrnp+bVef0ORNH8+B557DldeXrnnbAkJ7H7wQYpSU70QWcMm3RxCiFpnzbMDYLaY0dfTZ3jnnh2kZ6Rx0dVjURQ3RqORa68aDVD6b0BAYJltDAZDpeUUxbO4lsPhAOBk4gnSM9Lp1aN3pTGYTJ6uCafTCUBR8bZnJgc+Pj5APZ8s6cDHzwdXng1bfmH97VdjokaPZs9jj5Hw/vt0fPppUFWcOTlsv/12Ts6ZQ7v77sMnMtLbYTY42m2ZkGRCiAbLUej5MTWa6nbWwN79u5n5xUe89f7r/N/9kzAYDNx52130i+uPw+EgIyuDbrHd+XnhAn745Tv8fP3QFS8FmZ6RRvu2HSot1zfOM83009n/Y/feXUyacisPPz0Vg8FQuv2ZAzYvGXopAG++/xp79+/htXdeBmB48ePn8sEn7zF56h24XM5aOT5nMpo9sZe8Pk2R3seHTk89hSMzk72PPYYjLY0TX3zBidmzwWCg41NPeTvEBkmzLRMyZkIIcS6bt21i87ZNGAwGOrSL4cVn/0PvnnH06NaTbTu38uJrzwEQEd6c99/4GPC0RvSL68+ipb8TFhrG6FFjKyw3cvjl3DD6JuZ9P5d5388lJDiE/737WZntI8KbM/aacaXxDOp/EY/883He/egtfvj5O3Q6HXffcS+XX3pllf6e7Tu3sWTFH7jdCsY6/HZu6t+vbe66i4MvvYQjJaX0WOiAdlOmYGnZ0rvBNVA6VaPvKmdBAV8EBjL044/pOmVKve136/jxFBw8yMVbttTbPoVobA7vPMGSL//CP9iP4eMG1mrdObk5rFqzokplrdYCrHYb4WHh6PVlG2KLigrR6fWYTeazliuwFpCbm0PziMgy3RWnb3+mktaOsJAwLMVjLKqjS6cudO3Svdrbnctfv2wmNyOfS245vK3eAAAgAElEQVQaSGz/DrVef0Ny9P332T11aul9ndnMyCNHsERHezGqhkuzLRPSzSGEqIiqqrjcriqV9bFY8LFYUFSl9AJjJQzFp/4ut+us5SwWCxZLVGnZirY/k96gp3lE80qfPxdF0eQ5XqPSdvJkDr38Mo6UFNDpPK0SkkicN80mExptMBFCeFloSCjXX3ejt8MQDZzex4dOzzzD7qlT0ZlMdHzySW+H1KBpdwBmMZkaKoQQoi60vesufFq2pN0990irRA1ptmVCujmEEEJUhcNVyN7kLRzPiqegMBdFVc69UbHC/95KSrNmbF//TrX26WvyIyKwJd1a9iU8oEV1Q250JJkQQgjRYO1L3srCXV9jc5znQlxmID8Xzmfz5C2sPvQbca2HcEWPmzDqa3+xsYZCs8mEjJkQQghxNjtO/M0vO74EVHxMfrQMaY+/T1C97NvpdpKWd5xsaxpbj68hx57B+AEPoNdpfvRAndBsMlFCxkxUjxwvoQVxnQcy+ZoHvR2GaMRybBn8vmsuoBIdGkNcu0swGuq3ZaBrdH9OZB1k69GVHEnfx9+HFzOk41X1GoNWaDeZkG6O8yatOsLbStaZqEhGZjpr/l5Nt9judOkUW8+RnYrh7w1ribugD61btfFKDBXJzMpg0dKFDOg7kM4du1RYJis7iz//WknvC+Jo39azVoSiKKxdv4bjJ4/TKro1QwYNLV2ps7Fad2QpbsVFsG8z4toPR6/To6jueo8jOjSGwqIC9iRuYN3hJVzY4TIMeu3+tNYVzbbHyA+iEI1T/JFDPPTUP1my/A+vxbD/4D6mPn4v64uv1qkVx08cY9qMJ1m3cW2lZY4dP8pDT/2TtetPJWuPP/swEybfzL9fmsbEu8dz3fh/YLVZ6yNkrzmUuguA9pE9AZ1njRAv3dpGdMOgN1HotHEy+4h3D4yXaDaZKCHN9kI0bk6nk7/WrWbx8j9ITk2usExi0kkOHNqPzW4D4NiJBA4c2l9uQSiHw8G2HVtYtWYFqWkpZZ5zu91s27mVHbu3lzlZSTh+lIPxB0rvZ2Vnle5LURS27dzK0pWLy1yHw2a3ceDQfqw2K7v37iIlNeWsf0dFcVVUR6eYzsyf9QNXjPjHOf+e04/N9z9/yy03TCB++zGm3vMQu/bsZOXq5Wc97g2ZqqrkFWYDEOgXgoLbqzf0EGDxXB03x5bhzUPjNdpti5FuDiEavbz8PEaPv5rE5JOeZa1tVt599QOuueq6MuV27d3JlAf/j/HX38qUO+9j5LXDGTxwMF/OnFdaJis7ixsmjib+yCF0Oh06nY7XX3yb66+7EZfbxW2Tx/P3Bs/ZfLfYU0tVv/+/d/nup/ms+G0NMe078sJr0/ll4c/8tWQDEx8bz6atGwHwtfjy4VszuXTYSLZu38ytd93E8KGXsmrNCl59/g1mzvq4wr+jsriimkeVqeOjtz+hRWQLbrp9HDOmvcQ1V42ucLuYdjFljo2fnz+z//c13bp0Q6fTERwUAoBbqf8m//qiqG7U4umfenSl//emkmXYXUr1VzxtDLTbMiHJhBCN3uLlizh8NJ7XZrzJtr92M3rUWPYd2Fuu3JUj/8GoK69l/o/zePCJ+/H19eXVF94qU2bjlvXo9Xrmz/qR3esPEBoSxvc/fQvAL7//xN8b/mLy7ffw1+INhIeFl243pvhCXavWrET9//buPD6mq3/g+Gcy2TdJ7CKWkFhiJ/atxUMttf9Qa1VbSmmVVktLrVVLeVBabVGUlloiqAcllthCrCFNxZIQJYnIInvO74/IVCRkz4zk+35e83rmzr1nueY09zv3nHuOUngdO8yrbTuwa88Ozpw7zbpVGzl/3A+nipWYOjP9LIl//e3P1zMX8SD0wXPP40X1ejqPhvUbZ/t8nmZvZ0/71q9QpnRZdu7extdL5uLeqCldntzdKOpSuxqS9f4q7j3zBntnQsZMCFH0VXd2wVhrzPiPx/LNikW0admOoYNGZHrszKlzOXbiCBcun2fejAWUL5t+oqAuHbsSEfGQ9ZvXctXfj7DwUOLiUgdXXrh8HoAxb42lpEMpRg59myPeXgC0aNqKcmXL4XXsTxo3bEJYeCi9e/Rl196dGBkZsW7TGti0huiYaO7eu8v9B//oyhzz1jgG9H0D34vnWPLtokzP40X1ejoPgHv37mbrfDLzKPIREz/7gFbNWvHD8nWYmmZcgKwo0nU1ZCIyIooDu/7k8lk/Htx7QFxcAja21lSv5UybTq2o2yQ/F1Mr3tcsw70z8YSMmRCi6GpYrxH7Pbz4cOwkSjqUZP3mtQx9e2Cmx977J4TomNRBhVeuXsqwf90vP/HJ9ElUdqrC/JmLqFSxsm5f2o+T5OSMFx0jIyNe79qbkz4n2Lt/N7Y2tnRs/x+0Wi3Gxsa8/lovXn+tFx9P+JQlXy3H2tpGl9bmyfsXnceL6vV0Hjk5n8wkJMTzRv8hTHz/42ITSEDqd6tUSobXrz9u5XX3/iz4bAl7f/8fPsd9uXz2CicOnWL9t5t4p/c4xvSbQNCNoEzT5/QlwYShkm4OIYq8Rcu+pnPvV6hfpwE/LF9Ho/pNMh1omJiYyMTPxlPC1pYer/Vk42/rOX7yaLpjLl25iEajYcjA4RgZabgbcke3r1mT5gAsXr6Ac+d9+PaHZenS9unRl/j4eNZt/IlunXtgampKk4buuqXEa9d0Y+ee7fzusQVLC8scnceL6vUiOU0XFR1NZNQjHj4Mz1b+RYXKpMth/be/sGTGCuLj4l+Y9vypi7zT+31uXr+V926OQjpfQyXdHEIIvRk2aAQHDv+PEWOGAGBpYcmXn83OcNw3KxZy7a+rzJuxgP+82pk/jxxk8ucT2b/jEFZW1gD07NabXX/spEWHxrjVqkP1ai6E/HOXuLg4uv6nO/16/h+btm5k8++/0KVj+vEEtWq4UdO1Ftf+ukrvHv0AGDJwOL4XzzH76xkAlC5VhuULV+X4PJ5br/i4F/7b5DRdWHgoOzy34VazDq+07fDCvIuSlCf/S3Mv+B9+WLROt21sbEzb11pRq34NzMxNCb55l4Mehwm7nxp0RYQ94quPF7F866I81qR4X7M0ykCv2o/v3WND+fK8umED1QcPLrRyzw0aRPRff9H27NlCKzM/aTQaCcSE3qVNWmVVwpL2fZu98FilFCH37hLzOIaKjk5YmFvkutyoqEgehIVStXLVTLtIH4Tex8zMHFub7E+5HP4wnIhHEVR2qvzCiaBedB5Z1Su355NTxzx8eBQaxSsDmlHT3TnP+elLckoSc/eMBcDdtSMWZla6fb+s3Mr389fotues/pxWnZqnSx8TFcP7/T8m8NpN3Wdr96+kikvuJzA7H3iMyJgwutYdTOPKbXOdz8vKYO9MSDeHEMWDRqOhQvn8Wf7ZxsYWmxcECqVLlclxng72DjjYO2R53IvOI6t6PU9u0xUnirQxC6mCAoN07y0szWnZsWmGR0ctrS0Y9G5f5nz4790I/0sBVK5eMQ8VKd4/4gw/mBBCCFEoEhMTMTF5uVa+VM88zWGk/XcoYOzjOK6cv0qtBq4Z0rXq3JQf9v1Xt12ytP1znwrJXj2K9zXLcAdgppE7E0IIUaAOHDhAnz59SEhI0HdVcixFqfRTW7s6pds/afAXLPp0BUf3nSAi/JHuOBNzEyq7VNS9rO2s8jSldnFnsHcm0vr95dFQIYQoGAcOHGDy5MmcP3+euXPnYmVllXUiA6NUMuqpBb5e6dGSn5duJvpR6mPEcY/j2LN5P3s270ej0VDF1Ym6TWtTv5kbjVrXw8om49M5uaxJPuXzcjLYYELGTAghRMHw8/Nj0Dt9CQwMJDIyEhsbG8aPH6/vauVK6rMc/wYTNg5WTFsxkdnvLSY6Mv1iZ0opbvjf5ob/bTzW/4GJqQmv9mzNyI8HUcIhb2NTincoYcDdHPJEghBCFIwFCxZw/vx5IiMj0Wg0REVFYW1trVsDxNBf5ubmunNRmXQ51Gtei9UHFjH0w/5UqeH03H+HxIRE9m05xIf9PudhWEQeuzmK9zXLcO9MPCHdHEIUb0lJiezYvR3/gGtYWVrR6ZXOuNWqo+9qvdSmTJnC3GVx+Pn5ER0djbW1NXfv3sXGJvPZOA3N04+Gpk0a9SwbeysGvPc6A957nYcPHuF31h+/swFcPnONQL9b6X6w3r31Dz8v/o2xs97kwgk//tzxZEG4Ri50HvAKAMf2nubM4dRp2V/p2YoGLdNPxS0DMA2VdHMIUewlJSXSb1hvPvpsAjt3b2fZqiV06/8fNv62vlDKT05O5pXurVm2akmhlFdYatSowalTp9i9ezdNmzYlOjqapUuX6rtauaJQqY+HvuBlV9qGll2aMGrqIJbs+JK1x5fQY1indPl47/dBkULNRtW45vs3B7cdZdXMnwkJ+ofoyGhWfLGGg9uOcs03gFqNqmUoo7gz/GBCCFFseZ/2xvfCWeZO/5rTh3w5f/wKzlWqsWjZ19lKH3jjep7Kv/dPCIE3rnPjdiARjyJylceNW4H8HRig2w4LD8M/4BqxcbEApKSk4HvxHPsP7SMsPEx3XGJiIsdOHGHfwT8I+SckT+fxPG3btuXUqVMcOXKEixcvEhUVVSDlFKRnp7Ve8cUaFn60koUfreSX/27LdOpru1I2jJo2iNpN/n1k9FFYJImJCRibGjF+3ptojDQkJiTx49xf2LxiB1ER0WiMNIz/6i2MzbQZ8izu1yyD7+aQOxNCFF9pt6J9fE/TtHEzXKq5suXn7URFR6GUyrIbdMg7A7G1KcGAPgPp1b0v9nb2QOoFPSo6MsPxJWztdMcA9B/eG4Dfd27BytKaWdPm5vgclny7iB2e2zhxwIcK5R358qsv2Lvfk3NHLhGHhiFvD+DMudMAWJhb8O3i72nSqCm9BnXjTkgwpiamxDyOYen8FfR4rWeOy8+ONm3a0KZNG5KSkgok/4KknplO+/Jpf4KvpwZf5hZmdH+zI5bWmc+qqjX+9/e0mYUpGmMNKaTg2siZHsM74rFmPycPnEP7ZO6Knm/+B9eGVdOV9289ijeDvTMhj4YKIVq4t6S5e0u2eWyl4+vtaNy2HjPnTycqKipbfxs++eBTSjqUZNbXM3BvX5/3Jr7D4WOHWLB0Hu1ea5nhtWL1f9OlP7jrCABDBgxj+qczc3UOvbr1AeDA4f0opTh2wotX23bExsaWnzet4cy506xbtZHzx/1wqliJqTM/Yd/BvVy/8Tdfz1yE77HL9Oreh6v+frkqPyeMjQ3/9+WzUmfATNa96rWoqdsXFxvPis/WEhMVk+4YpZI5vOM4l0/56451c3dNt/+ND3pSvnLqjKnJySk4Opdj0IQeGfL599HU4h1OGG7LkTETQhR7pqambF6zFR/fMxz19uLkGW927t7OgcP78dpznFIlS78wfc9ufejZrQ8PQu/j+YcH2zy2MvzdN7CytGLZgpUZjq/u7JJuO202SCMjLcba3P25bNOqHQ72Dvx55AD16zYgLDyMnt1S73j4XjyHkZER6zatgU1riI6J5u69u9jalMBYa8z4j8fyzYpFtGnZjqGDRuSq/KLu2QGYPUZ24ODW48THpU7A5f3HWc4duYxrg6rY2FsTFxPPLf87hIb8u7qqRqOhz+jO6fLRmmpwKGtHyK37ANg6WGNkosl0sCfIAEyDDSbk0VAhxPGTR/njwF5Gj3yPieMmA/DdT98yd9Esrly7Qo3qNUlOTsKxQkUeRT7iYUQ4To6VMizIdTv4FoE3A7kTEgyk/n25fuPvDOXZPdXFkV+MtcZ069yD37ZvppZrbaytbejQLnXwn1arxdjYmNdf6wWg+/82Lduy38MLjz3bOertxfrNazlx+jgHPLzyvX4vu5QnHR1pSlawY+J/R7Fo/GoS4hIBiHscz0Xva5mm12g0jJzWH9dG6bsv9v1yhCun/9JtX/X5mz0bDvHa0PYFcyIvOYMNJtJIN4cQxZdWa8zPm9Zw81Yg7458j6TkZPYe2IOJiQmu1Wtw9IQXk6d9yLYNHkyb/SllSpdl7coNuvTLv1/Kpq0bCb4TRNky5fi/3oP4v94D+eHn71jybcYlp98eMZq2LdvptjWk/v15EHqfsPAwSjqUzNV59OzWh/Wb17Fmww9069wDMzMzAJo0dGfX3p2EhofStmU75i2eTVJSEoE3r7PqpxV8v/Qnhr8xkrfGDifgun8WpRRPKpNHQ+u3qcGCnVPYvMSTMwcukpSY+d0El/pVGPRRD2q7V0+Xx/2gMDYs3AGAWzMXHkfFcsMvmF8WedCgbS3KViqVWU3y7ZxeRoYbTEg3hxDFXnP3Fnz+yZd8s2Ihg0cNAFJX8Vw0dynly5anf68BeP7hwZvvDSUlJYWfVqR/ZPT3nb9Rz60+s6fNo22r9ro7FnOnf83c6Vk/EaLVamnS0J29+3dTulSZXA3AhNSgoaKjE8F3gujZvY/u8yEDh+N78Ryzv54BpK5qunzhKqpVrcaBw/9jxJghAFhaWPLlZ7NzVXZRp1T6VUPTlHFyYPyiYcRGx3H98m3uB4URGx2PsakWu9K2VKtTiVIV7HV5/JufYuXUjcTHJmCkNWL4p715HB3LjCHLiI9LYNXUX/h83dgMP3SL+810ww8mhBDF2qhh7zBs4AiC7txGgwanipXSrWw5dOBwDh/9k749+1OubLl0aXdv3Y+lRd7WXvh9gwcxMdEYPdN1khMajYZqVaoRHx9Pq2atdZ8ba41ZOn8F06fMIuJRBJWdKusCnj1b9xNy7y4xj2Oo6OiEhXnmTyQUd89Op/0sM2sTajevRu3m1TJJmzHdvo3HuHom9ZHiV/s3x9E1dRBm8y71OfnHBa76XOePjUfoPKT1MymL9zXLcIOJNHJnQohiz9TUlGpVq2f4PCEhgXmLZlPDpSY7PLcxdMBwGtZvrNuf10AijZWVdZ7Sd+rZnr/+9ufDsZMyjOeA1LstDvYO6T7TaDRUKO+Yp3KLg/xetbPT4JZ0GtwyXf4AYxcNZuyiwRk+TyMDMA2UPBoqhMjKwmXzCfknBK89xxk3aTQTp05g79YD6dZuMATdu7xO+XIV6N29r76rUuSkPpppsLMcFBuFFkzExsaxY6cH3idPEhmZ9SxryfHxRDRy59C6DZju2JWtMkqVLEnbNq3p1rXLS/m8tBAiZz776HM+++hzAH5du03PtXm+CWMm6rsKRYqRRotGY5S6yNczk1bpi0pJ/QFsrDXJ4siiqVCuuMF37jDhw0kEB9/JWUIzcx6E3IOQe9lOcuy4Nzt2evDNogXY2ZXIYU2FEEIYOo1GQwkLByIehxIfF5tuJkt9UEqRmJg6r4WdRe6e+HnZFXgwkZyczMeffEZw8B1sLS3p364NVcuVxSifuy+SklPwDwpi69Hj+F29xoyZs1myeEG+liGEEMIwuJSpy5mbh4h6FIWFpTnosUc86lE0SiksTKyoaO+sv4roUYEHE4cOH+F64A0szExZPXECVcqWKbCyOjdpROu6dRi77FtOnDzFFb+ruNWuVWDlCSGE0I8W1TrhG3SMxMREwkLDsStVQi9j7B5HxxIZkdp137J6Z7RGxbOLvcDP2tc3df33VxvUL9BAIk2j6tVo4FyVc39fx9f3vAQTQqC/gczrZu7QS7mi6CthUZLu9Yay8/wa4h7H88+dB5hbmKLVagulvSenpJAQl0BSQurjpdXL1KGFc6csUhVdBR5MRD5Z0ra0nV1BF6VTxj61rMjIjKsCClFcFeYU9dcvBjFh9IeFVp4onuo6NsPM2JzdFzcSHf+I2Oj4Qq+DkcaIxpXb0al2PzSa4vtUSaHdjynM30UafXaeCSEA6NayYB+DTEpKZMfu7fgHXMPK0opOr3TGrVadAi1TGB7XsvWp+motrt3z5XZYAHGJjwulXK2RMaVtKlC7QmPsLV+84FxxUDw7d4QQL7WkpET6DeuN74WzlC1TjrCwUJZ8u4g5X8xn8P8NzbdykpOT6dizHX269+P90R/kW74if5loTanr2Iy6js30XZViq/jekxFCvLS8T3vje+Esc6d/zelDvpw/fgXnKtVYtCzr9TZeJPDG9XTb9/4JIfDGdW7cDiTiUUSu8oyNi8U/4BrR0VFc9rvEEW8vYh7HpDvmqv8V9vzPk0tXLua67kLok9yZEEK8dNLGf/j4nqZp42a4VHNly8/biYqOQimVowF4D0Lv8/vOLfy2fTOmpmb8se2Abl//4b0B+H3nFqwsrXO10Ndlv0v0G9qTBnUbcv6SLwAVylVg345D2NrY8uGn77PNY6vu+M4durDymx8ynXZbCENlMMHEn2fO8PPu3QC0ql+ft3un/ke85cABdh87BsCQrl3p2LSp3uoohDAMLdxb0ty9Jds8trLNYyulSpamdYs2jBr2brYCiaSkRA4ePsCv2zZx+NifaNDQumVb3ug3JN1xB3cdoWZjZ4YMGMb0T2fmqc42NracO3qZlT8uZ/XaVZzyOUFKimKbx1bef3cC4975gNVrV7Fw2Xy27dpK/14D8lSeEIXJYLo5Wtavz8lLl1jn6cn7CxYQeOcOEVFRjJk3j3Wenpy4eJFW9evru5pCCANgamrK5jVb2bp+JxPGTKRa1Wrs3L2dgSP7ERr2IMv03fp35p0JI3n46CEzPp3NGa8LrFu1kc4dX0t3XNrqpEZGWoy1efvt1aVjV0o6lKS5ewsAoqOjOeXjDcCwQW9ibm7OyKGjADjtczJPZQlR2AwmmDA3NeWn6dMxMjIiPiGBj775htk//kjYo0cYGRmxdsYMLMzM9F1NIUQ2FPTzVMdPHuWLOVOpUK4CE8dN5rd12/l04jSio6M4dvIod+4GA/Ao8hE3b98gOTn9UtNlS5cFICj4NjdvBxJ053YB1xjMnvz9evrOiYmJKQCJiYkAxCckPPm8YNd3kAUURX4zmG4OgJb16jFh4EC++eUXdhw+jPGTPsOJgwfTol69HOcXvH49x9avy1GamIAALCpVynFZQoh/mVqkXgyTEpIKJH+t1pifN63h5q1A3h35HknJyew9sAcTExNCQx/Q6j9N2bbBg2mzP6VM6bKsXbkhXfqfv9/Etb+usmX7ZrZ7/s6PP6+mamVnhr/xJm8OGaU7Lu0x8weh9wkLD6OkQ/6uu/BKm1dZ9eMKFi3/mlHD3uXnTWsAaN/m1XwtJ01ifOr3kfb9CJFfDCqYAJgzdiyex44RcPs2ScnJ1KxShVljxuQqL62VFWY2VjlKY1auHFbVquWqPCFEKksbCwAS4hNRKSlojPL3Jmhz9xZ8/smXfLNiIYNHpY4tcLB3YNHcpfTs2pujJ47w5ntDSUlJ4acV6zPNo6ZrLT7/5Es+/WgaB70OsmX7ZrZ5bE0XTGi1Wpo0dGfv/t2ULlUmVwMwX3weLZk4bjJLVy7m951b0Gg0vPPmGP7zapd8LQcABQmxqXc+0r4fIfKLwQUT5qamOJYuTcDt1NuOpe3tMcvlLb/yffrgPubd/KyeECIbbEtaozXWkpyUzMP7kTiUy/8ZcEcNe4dhA0cQdOc2GjQ4Vayk6x4YOnA4h4/+Sd+e/SlXttwL8zE2NqFzhy507tCFx7EZJzz6fYMHMTHRGOXy6Qr3Rk25dSVEt92hXad02xPGTOTtEaO5HXSLihUqYm1tk6tysvIoLIqkpGSMjDTYlS6YMkTxZTBjJtJ8u2ULh8+e1W0f9fVl2a+/6rFGQoicMjbR4uiSOi4h5GbWAyJzy9TUlGpVq+NctZoukEhISGDeotnUcKnJDs9t+F44m0Uu/7K0sMz0cysrayzMC+7XvKWFJTVdaxVYIAFw78n3UK5qacwsTAusHFE8GVQwEXjnDp8sWwbAK02a0KhmTQA+Xb6c68HB+qyaECKHqtdPHXsU5H+XuMeFt2bCwmXzCfknhI0//Ip7o6ZMnDqBuLi4QivfECXEJXLz2h3g3+9FiPxkMMGEUoqRX35JTGwsWiMjlk6axNJJkwB4HBfHyJkzC3WhIiFE3rg0qoJdGVuSk1PwO/U3FNJ/vp999Dl+pwMoXaoMv67dxiHPY5ibmxdO4Qbq2pnrJCUkYeNgRc2mMiZM5D+DCSaW/forXufOAfBOnz7UrV6d1g0a8H+dUpd0PXLunHR3CPESMTLS0KpHQ9BAyI37/H3hlr6rVCzduBJMUEDqGI0W3RqgNTaYP/uiCDGYVjV+4ECUjw/Kx4dvp0zRff7rvHm6z8cPHKjHGhompRR79uwhJSUlw76DBw/y+HHhrKAnRGYq1apAk06pK3n6nwvE79TfpMgdxkKhlMLfJxC/0wEA1G9Xk2rSxSEKiMEEEyJ3NBoNJ0+exMXFhc2bNwOwf/9+GjZsyPfff4+lZeYDyoQoLO6d6uLW0gWAG1eCOL7Th/tBoYXW7VHsKHhw9yHHd53l74u3QEGNJlVp0a2BvmsmijCDezRU5NyHH37IggULGDFiBKampvTs2ZO4uDg2bNiQdWIhCpoG2vZpgkO5Enh7nCMyPJoz+y9hZmlGmYolsS5hiZmFKUZamZUxt1SKIi42gZiIGB7cCSc2JnXAq5HWiOZd61O/XU0911AUdQUeTGifTFaTnJzxNnxBSXoydW5unwt/2djb2zNhwgT++9//Ehsbi1arpVu3bri5uem7akLo1GnpQpXajpzZd4m/zt4g/nE8QX/d1Xe1iiSNkYbq9SvRtEs9bEta67s6ohgo8GCiXPnUCWMu37xZ0EUBkKIUV28HAVC+3IsnqylKpkyZwvLly4HUNQDmzs3fmfrEy+XWrVssW7aMSZMmUe7JfwdKKTw9Pbl27RqTJ0/WS72s7Sx5ZUAzWr7ekNvXQrjz9z/ERMYSG1W8H93MDxbWZljaWlDBuQyVa1XA3ErWMhKFp8CDiVdfacfades59/d1Nhz4k8EdXimwRWaSkpP573YPgkNDMTMzo3WrlgVSjiGys7Nj3IHqNJAAABMSSURBVLhxLFy4kFdffZW6devqu0pCjypXrkxQUBCVKlXi7bffBqBmzZoEBARw+fJlPdcOzCxMcWlYGZeGlfVdFSFEPtCoQpi8YcGib9j6+3YAypd0wKlUqQIp53pICGGRUQC8P3YMQwYPKpByDFV4eDhVq1blxIkT1K5dW9/VEXrm7++Pm5sb2ifdfUlJSXTt2pVdu3bpuWZCiKKmUIKJlJQUVn3/A79s+lW31G5BsbCwYPQ7oxg4oH+BlmOovL29admy+NyRES/Wt29fPDw8SEpKwsLCglOnTsldKyFEviuUYCJNWFg4Z3zOEhYWRnJKcr7mbaw1pmzZMjR1b4KNjSxiIwRAQEAADRo0IC4uTu5KCCEKTKEGE0KIwte/f39+//13Ll26JE/4CCEKhMwzIUQRN2fOHIyNjSWQEEIUGLkzIUQxEBMTg5WVlb6rIYQooiSYEEIIIUSeyNocQgghhMgTCSaEEEIIkScSTAghhBAiTySYEEIIIUSeSDAhhBBCiDyRYEIIIYQQeSLBhBBCCCHyRGbANCCenp7s2LEDAI1Gw8KFCylRokS6Y/z9/VmwYIFue8aMGVSsWLFQ6yn0y8PDAw8PDwBKlSrFV199leGY3bt3s3176kq9I0eOlMXfhBAFSoIJA+Lu7s6gQYOIjo4GoEqVKkydOjXdMfPnz2fNmjUAtG/fXgKJYujcuXP8+OOPuu3GjRvTv3/6VXJ9fX11x7Ru3VqCCSFEgZJuDgNStmxZPv74Y9320qVLefz4sW47JCSEjRs36rYz+0Uqip+PP/6Y+Ph4fVdDCFGMSTBhYD766CMcHR0BePDgAatXr9btW7p0KQkJCQD07duXZs2apUubkpLCw4cPSU7Oenn3uLg4Hjx4QEpKSj7WXujDzZs3Wbx4cY7TpaSkEBISws2bN4mNjS2AmgkhigsJJgyMpaUls2bN0m0vXLiQxMREoqKi+O677wDQarXMmTNHd4y/vz99+/bF2toaBwcHzMzMaN++PQcPHsyQ/759+2jWrBlWVlaUKVMGMzMzGjVqxI8//ogs0/LysbS0BGDevHncu3cvW2kePnzIuHHjKF26NBUqVKBq1arY2trSsWNHTp8+XZDVFUIUURJMGKDhw4dTv359AIKDg/n5559ZvXo1ERERQOqAuho1agDg4+ND06ZN2bZtm+7XZXJyMl5eXnTq1IkNGzbo8t21axddu3bl9OnTujsSSUlJ+Pr6MmrUKD799NPCPE2RDyZOnIhWqyUqKirD+JrMREZG0qZNG1asWEF4eLju86SkJA4ePEjbtm35888/C7LKQogiSIIJA2RkZMTChQt12/Pnz2fJkiUAWFhYMH36dACUUgwbNozIyEgAxo8fz969e5k9ezZmZmYopRg9ejT3798HYMqUKaSkpFCiRAmOHj3KgwcP2LdvH3Z2dkBqN0pMTExhnqrIIzc3N0aPHg3A2rVrOX/+/AuPnzp1KleuXAHAxcWFtWvXsnPnTvr27QtAfHw8w4cPlzEYQoicUcJgdenSRQHpXp988olu/7Fjx3Sf9+jRI13aL774QrdvyZIlSimljI2NFaAcHBzUuXPndMdu2bJFzZo1S82aNUvdv3+/cE5O5Nr06dN13+2mTZtUaGiosre3V4Bq166dUkqpWbNm6Y5Zs2aNUkqpmJgYZWlpqQBlamqqgoOD0+XbunVrXZqNGzcW8lkJIV5mcmfCgC1cuBCtVqvbtre3Z8qUKbrtM2fO6N4nJSUxe/Zs3evWrVsZjqtZsyYA4eHhNGrUCCcnJ9544w2ioqIYO3Ys06ZNo3Tp0gV9WiKflSxZki+//BIALy8vtm3blulxPj4+uqeD2rVrpxvom2bIkCG690eOHCmg2gohiiIJJgyYm5sbvXv31m2PHj1a1yUBEBYWpnu/d+9ePv/8c91r3bp1un1pYy1WrlxJqVKldJ8HBwezadMmRo4ciZOTEz/88ENBno4oQGPGjKF27doATJ48WffUz9MePHige/9sIPHsZ2ldY0IIkR0yaZWBK1mypO69g4NDun0WFha692+99RatW7fONI+0ia1at27NjRs32Lp1K3v27MHLy0t30YiJieHdd9+lYcOGNG7cOL9PQxQwY2NjFi9eTJcuXQgMDMw0MLS2tta9f3rwZZqng1MbG5uCqagQokiSYOIl5uLiontvY2PDiBEjdNsREREEBwcDqVMuBwQEsHPnTiB1xsTffvsNpRQXLlxgypQp7Nu3j5SUFA4ePCjBxEuqc+fOdOvWjd27dxMSEpJhf8OGDdFoNCil8Pb2JjY2Nl1AeuDAAd37Ro0aFUqdhRBFg3RzvMQ6duyo+7X57bffsmvXLpRS3L17ly5dulC3bl3q1q3LwYMHiYiIYPLkyUyePJlx48bxzz//oNFoaNCgQbqpluUX6ctt8eLFmJiYZLqvTJkyvPbaawCEhoYyYsQIbt++TWRkJCtWrGDTpk0AmJubM3DgwEKrsxDi5SfBxEvM3t5eN6V2QkICr7/+Oubm5jg6OnLq1CkA2rZty8CBA2nSpAnNmzcHwM/Pj0qVKuHq6kqlSpV0j5ra29vTr18//ZyMyBeurq68//77z93/zTffYGtrC8Bvv/1G5cqVKVGiBOPGjdPNnPrVV19RtmzZQqmvEKJokGDiJTd27Fg2btyoGxeRNvBOo9EwZMgQdu/ejVarRaPRsHPnTrp3745GoyEhIYGAgACCgoIAqFOnDv/73//kaY4i4Isvvnju9+jq6sqhQ4do0KBBhn12dnYsX76cCRMmFHQVhRBFjEYpmUO5KEhKSsLHx4fbt29jbW1NkyZNKFOmTKbHhoSEcOHCBSIiIrCwsMDV1ZVatWoVco2Fvvn5+XHt2jUSEhJwdHSkadOmmJmZ6btaQoiXkAQTQgghhMgT6eYQQgghRJ5IMCGEEEKIPJFgQgghhBB5IsGEEEIIIfJEggkhhBBC5Il2xowZM/RdicL2448/cuTIEUqVKpVuvYsdO3bwxx9/AP+uZyHSW7lyJcePH+fkyZOUL19et/BYUlISy5Yt4+TJk5w8eZIaNWqkm6o5PDycCRMmYG5uTrVq1Z6b/61bt1i7di1RUVFUq1aN1atX8+uvv9KxY0c0Gk2Bn5+0jbzJTfvIbtsACr09CCGySY/Ln+uNo6OjAtSWLVvSfd65c2cFqOnTp+unYtk0YsQI1bhxY3Xp0qVCz7tEiRIKUIBauHCh7nMfHx/d54C6evVqunTDhg1TFhYW6v79+y8s/8SJEwpQH3zwgVJKqcOHDytALVu2LI9nlj3SNvKWd27aR3bbhlKF3x6EENkj3RwvoatXr3L27Fmio6P1lrednR3Hjh3TbR8/fhxAN1Xz03x9fVm/fj1Dhw7NcobNtP1p/9+uXTvc3d2ZPn06kZGROTqX4sgQ2gZkv33kpG2AtAchDJUEEy/g7+9Pv379qFKlCs7OzgwYMICrV6/q9t+7d4+33nqLqlWr4uzszMiRI7lz585z83v8+DGTJk3C2dkZW1tb3NzcWLx4MerJvGGhoaF06dKF3r1769IEBgbSpUsXRo4cCcDHH3/M9evXAZgxYwaenp66dGPHjsXDw4NmzZrRsGFDpk+fTmJiYp7yfp5mzZrpLhAA3t7euLi4YG9vn+HYRYsWoZRi4MCBbN26le7duzNo0CASEhJQSvHWW2/RvXt3fvvtN90F5enZOwcMGEB4eDhr1qx5bn0KW363DXhx+3iZ2gZkv3083TaALNsHGGZ7EKLY0+dtEX3Jzq3s6OhoVbFiRWVsbKz69eunOnTooABVvnx5FRcXp6KiopSLi4sClKurq6pevboCVPXq1VV0dHSm5fbt21cBqkSJEqpp06bKzMxMAWrWrFlKKaWCgoIUoKysrHRpfH19FaCqVaumy8PKykoBqmbNmmrt2rW6dCVKlFAmJiaqVKlSutvJI0aMyFPez0q7jT1jxgwFqL/++ksppVTFihXVsGHDVOXKldPdxo6Pj1e2trbKzMxMxcfHq5iYGOXs7KwANX/+fLV+/XoFKGdnZ92/m4uLi9q3b5+uzPPnzytAtW7dOgffcu7oq20o9eL28TK0DaVy1j6ebRtKqWy1j8JsD0KI7JFg4ilPXzC8vLwUoGrXrq0iIiKUUkp98sknavDgwer69etq5cqVClCdOnVSycnJKjk5WXdR+e677zKUeebMGQUoBwcHdffuXaWU0pVhZWWlwsPDs/VHXSmlmjVrpgB14sQJpdS/FwNA7dixQyml1N69exWgNBqNCgoKynXez0q7WOzbt08B6qefflK3b99WgFq1alWGYOL06dMKUFWrVtXlcejQIaXRaJSNjY1ydHRUGo1GeXl5Pff7Cg8PV4DSarUqMTHxucflB320DaWybh8XL140+LahVM7aR2ZtQ6ms20dhtgchRPZIN0cmNBoNVatWxdTUFD8/P0qVKkXz5s0xNTXlq6++wtnZmdOnTwOpI9FHjhzJyJEjCQsLA8DHxydDnt7e3gB07dqV8uXLA6nLg9eoUYOYmBjOnz+f53o7OjrSs2dPALp06YKbmxtKKa5du5bnvJ9Vp04dbGxsOH78uO52dosWLTIcFxwcDJBuSev27dszZswYoqKiuHPnDuPHj6dt27bPLcve3h4TExOSk5O5d+9ePp9JzhRE24CCbx+F2TYge+0js7YBWbcPQ2oPQohUxTKYsLKyAiA2Njbd52nbVlZWODk5cfjwYXr16oWZmRmnTp1i1qxZuLm5ERAQQFxcHACJiYmEhoYSGhqKo6Mj3bp1o2bNmhnKjImJAUj3uOTT29HR0Zk+6paSkpLt87K0tEy3bWNjA6Q+lpfXvJ9lZGRE06ZNOXbsGN7e3tjY2FCnTp3nlvHseVeqVEn3/nmrmz7N3Nw8z3XODn20Dci6faTtf5qhtg3IXvt4XtuArNtHYbUHIUT2FMtgomrVqgAcOHBA99mDBw+4cOGCbr+3tzdeXl6MHTuWhw8fcuTIERo3bkxkZCTbt2+nSpUqAHTo0AFPT088PT1ZsGABH3zwAb169cpQZu3atQE4ceKE7rPQ0FD8/f0BcHNz0/1RjYuL012QLl26lO3zunnzJnfv3gVSfxWnpXV2ds5z3plp0aIF/v7+eHp60qxZM4yMMjYnJycnAN0vc0h9KmD69OlYWFhgbGzMl19+yZUrV55bTkJCAlFRUWi1WipUqJCnOmdFH20Dsm4fTZo0AV6etgFZt4/M2gZk3T4Ksz0IIbJJ3/0s+rBhwwZdP3Lbtm3V0KFDlZOTkwJU6dKlVXR0tPLw8NANqvvuu+/U+vXrVf369RWgtm3bpi5evKiMjY2VsbGxmjZtmvrqq6+Ura3tc/v/ExMTdYPyXnvtNTVv3jxdfr169VJKKZWSkqLKlSunANWhQwc1fvx4XR/0033XrVq1UoAaNWqU8vHxSdcv7ubmpqZNm6bq1aunANWqVas85f2stDQhISFq9+7dunK/+OILpZTKMGYiMjJSmZmZKTs7O5WSkqKSkpKUu7u7bvzB+PHjFaCaNGmikpKSMv2+/vrrLwWoevXq5fIbzz59tA2lsm4fL0PbUCpn7ePZtqGUylb7KMz2IITInmIZTCil1OzZs3Wj09NetWvXVqdPn9YdM2nSJGViYqLbb2xsrMaNG6fb7+HhoSpVqqTbb21trVatWvXcMs+fP69cXV3TldmmTRt179493TGbN29WlpaWClCWlpZq5syZGf6oL1++XJmamipAzZs3T3fBcHJyUgMGDNDl7erqmm5yoNzk/aynLxZhYWFKo9EoQO3du1cplTGYUEqpnj17KkBduHBBzZkzRwGqYsWKKiYmRoWHh6uSJUsqQM2ZMyfTf7cffvhBAWr27NnP/bfNT/poG0pl3T4MvW0olfP28XTbUEplq30UdnsQQmRNo9STSQ6Kobi4OPz8/IiJiaF8+fJUr149wzEPHz7k+vXrKKVwdnamZMmS6fYnJycTEBBAZGQkderUydA3/SylFH5+foSFhVGxYkWcnZ0zHPP48WPu3r1LxYoVdX3Dz0pISCA8PBxbW1vCw8NxcnKicuXK3Lx5k8DAQGJjY6lVq1aGW8s5zTur88mO7du306dPH2bOnMnnn3+e4/Q9e/Zk9+7dBAQE6LohCpo+2gZk3T6kbeinPQghXqxYBxNFRXBwcLoLhqFRSuHu7k5ISAiBgYGYmZllO+3169epUaMGb775JqtXry7AWhZNRa1tSHsQwjAVywGYonBpNBp++uknUlJS2LhxY47SLl++nBo1arBgwYICqp3Qp5y2DWkPQhgmuTNRRMTFxaHRaHL0q18UD9I2hBAFTYIJIYQQQuSJdHMIIYQQIk8kmBBCCCFEnkgwIYQQQog8kWBCCCGEEHkiwYQQQggh8kSCCSGEEELkiQQTQgghhMgTY31XQAghhBAvH5W6WCgajUbuTAghhBAi55KTk5k6dSoxMTEyA6YQQgghci42NhZLS0vGjBkjwYQQQgghci4mJgZra2scHR35fyS3gOmoM3j0AAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Adversarial detection by matching prediction probabilities\n",
    "\n",
    "### Method\n",
    "\n",
    "The adversarial detector is based on [Adversarial Detection and Correction by Matching Prediction Distributions](https://arxiv.org/abs/2002.09364). Usually, autoencoders are trained to find a transformation $T$ that reconstructs the input instance $x$ as accurately as possible with loss functions that are suited to capture the similarities between x and $x'$ such as the mean squared reconstruction error. The novelty of the adversarial autoencoder (AE) detector relies on the use of a classification model-dependent loss function based on a distance metric in the output space of the model to train the autoencoder network. Given a classification model $M$ we optimise the weights of the autoencoder such that the [KL-divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) between the model predictions on $x$ and on $x'$ is minimised. Without the presence of a reconstruction loss term $x'$ simply tries to make sure that the prediction probabilities $M(x')$ and $M(x)$ match without caring about the proximity of $x'$ to $x$. As a result, $x'$ is allowed to live in different areas of the input feature space than $x$ with different decision boundary shapes with respect to the model $M$. The carefully crafted adversarial perturbation which is effective around x does not transfer to the new location of $x'$ in the feature space, and the attack is therefore neutralised. Training of the autoencoder is unsupervised since we only need access to the model prediction probabilities and the normal training instances. We do not require any knowledge about the underlying adversarial attack and the classifier weights are frozen during training. \n",
    "\n",
    "The detector can be used as follows:\n",
    "\n",
    "* An adversarial score $S$ is computed. $S$ equals the K-L divergence between the model predictions on $x$ and $x'$.\n",
    "\n",
    "* If $S$ is above a threshold (explicitly defined or inferred from training data), the instance is flagged as adversarial.\n",
    "\n",
    "* For adversarial instances, the model $M$ uses the reconstructed instance $x'$ to make a prediction. If the adversarial score is below the threshold, the model makes a prediction on the original instance $x$.\n",
    "\n",
    "This procedure is illustrated in the diagram below:\n",
    "\n",
    "![adversarialae.png](attachment:adversarialae.png)\n",
    "\n",
    "The method is very flexible and can also be used to detect common data corruptions and perturbations which negatively impact the model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# more imports\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "from alibi_detect.ad import AdversarialAE\n",
    "from alibi_detect.datasets import fetch_attack\n",
    "from alibi_detect.utils.fetching import fetch_tf_model\n",
    "from alibi_detect.utils.tensorflow import predict_batch"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Utility functions <a name=\"ad_utils\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# instance scaling and plotting utility functions\n",
    "def scale_by_instance(X: np.ndarray) -> np.ndarray:\n",
    "    mean_ = X.mean(axis=(1, 2, 3)).reshape(-1, 1, 1, 1)\n",
    "    std_ = X.std(axis=(1, 2, 3)).reshape(-1, 1, 1, 1)\n",
    "    return (X - mean_) / std_, mean_, std_\n",
    "\n",
    "\n",
    "def accuracy(y_true: np.ndarray, y_pred: np.ndarray) -> float:\n",
    "    return (y_true == y_pred).astype(int).sum() / y_true.shape[0]\n",
    "\n",
    "\n",
    "def plot_adversarial(idx: list,\n",
    "                     X: np.ndarray,\n",
    "                     y: np.ndarray,\n",
    "                     X_adv: np.ndarray, \n",
    "                     y_adv: np.ndarray,\n",
    "                     mean: np.ndarray, \n",
    "                     std: np.ndarray, \n",
    "                     score_x: np.ndarray = None,\n",
    "                     score_x_adv: np.ndarray = None,\n",
    "                     X_recon: np.ndarray = None,\n",
    "                     y_recon: np.ndarray = None,\n",
    "                     figsize: tuple = (10, 5)) -> None:\n",
    "    \n",
    "    # category map from class numbers to names\n",
    "    cifar10_map = {0: 'airplane', 1: 'automobile', 2: 'bird', 3: 'cat', 4: 'deer', 5: 'dog',\n",
    "                   6: 'frog', 7: 'horse', 8: 'ship', 9: 'truck'}\n",
    "    \n",
    "    nrows = len(idx)\n",
    "    ncols = 3 if isinstance(X_recon, np.ndarray) else 2\n",
    "    fig, ax = plt.subplots(nrows=nrows, ncols=ncols, figsize=figsize)\n",
    "    \n",
    "    n_subplot = 1\n",
    "    for i in idx:\n",
    "        \n",
    "        # rescale images in [0, 1]\n",
    "        X_adj = (X[i] * std[i] + mean[i]) / 255\n",
    "        X_adv_adj = (X_adv[i] * std[i] + mean[i]) / 255\n",
    "        if isinstance(X_recon, np.ndarray):\n",
    "            X_recon_adj = (X_recon[i] * std[i] + mean[i]) / 255\n",
    "        \n",
    "        # original image\n",
    "        plt.subplot(nrows, ncols, n_subplot)\n",
    "        plt.axis('off')\n",
    "        if i == idx[0]:\n",
    "            if isinstance(score_x, np.ndarray):\n",
    "                plt.title('CIFAR-10 Image \\n{}: {:.3f}'.format(cifar10_map[y[i]], score_x[i]))\n",
    "            else:\n",
    "                plt.title('CIFAR-10 Image \\n{}'.format(cifar10_map[y[i]]))\n",
    "        else:\n",
    "            if isinstance(score_x, np.ndarray):\n",
    "                plt.title('{}: {:.3f}'.format(cifar10_map[y[i]], score_x[i]))\n",
    "            else:\n",
    "                plt.title('{}'.format(cifar10_map[y[i]]))\n",
    "        plt.imshow(X_adj)\n",
    "        n_subplot += 1\n",
    "        \n",
    "        # adversarial image\n",
    "        plt.subplot(nrows, ncols, n_subplot)\n",
    "        plt.axis('off')\n",
    "        if i == idx[0]:\n",
    "            if isinstance(score_x_adv, np.ndarray):\n",
    "                plt.title('Adversarial \\n{}: {:.3f}'.format(cifar10_map[y_adv[i]], score_x_adv[i]))\n",
    "            else:\n",
    "                plt.title('Adversarial \\n{}'.format(cifar10_map[y_adv[i]]))\n",
    "        else:\n",
    "            if isinstance(score_x_adv, np.ndarray):\n",
    "                plt.title('{}: {:.3f}'.format(cifar10_map[y_adv[i]], score_x_adv[i]))\n",
    "            else:\n",
    "                plt.title('{}'.format(cifar10_map[y_adv[i]]))\n",
    "        plt.imshow(X_adv_adj)\n",
    "        n_subplot += 1\n",
    "     \n",
    "        # reconstructed image\n",
    "        if isinstance(X_recon, np.ndarray):\n",
    "            plt.subplot(nrows, ncols, n_subplot)\n",
    "            plt.axis('off')\n",
    "            if i == idx[0]:\n",
    "                plt.title('AE Reconstruction \\n{}'.format(cifar10_map[y_recon[i]]))\n",
    "            else:\n",
    "                plt.title('{}'.format(cifar10_map[y_recon[i]]))\n",
    "            plt.imshow(X_recon_adj)\n",
    "            n_subplot += 1\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "def plot_roc(roc_data: dict, figsize: tuple = (10,5)):\n",
    "    plot_labels = []\n",
    "    scores_attacks = []\n",
    "    labels_attacks = []\n",
    "    for k, v in roc_data.items():\n",
    "        if 'original' in k:\n",
    "            continue\n",
    "        score_x = roc_data[v['normal']]['scores']\n",
    "        y_pred = roc_data[v['normal']]['predictions']\n",
    "        score_v = v['scores']\n",
    "        y_pred_v = v['predictions']\n",
    "        labels_v = np.ones(score_x.shape[0])\n",
    "        idx_remove = np.where(y_pred == y_pred_v)[0]\n",
    "        labels_v = np.delete(labels_v, idx_remove)\n",
    "        score_v = np.delete(score_v, idx_remove)\n",
    "        scores = np.concatenate([score_x, score_v])\n",
    "        labels = np.concatenate([np.zeros(y_pred.shape[0]), labels_v]).astype(int)\n",
    "        scores_attacks.append(scores)\n",
    "        labels_attacks.append(labels)\n",
    "        plot_labels.append(k)\n",
    "    \n",
    "    for sc_att, la_att, plt_la in zip(scores_attacks, labels_attacks, plot_labels):\n",
    "        fpr, tpr, thresholds = roc_curve(la_att, sc_att)\n",
    "        roc_auc = auc(fpr, tpr)\n",
    "        label = str('{}: AUC = {:.2f}'.format(plt_la, roc_auc))\n",
    "        plt.plot(fpr, tpr, lw=1, label='{}: AUC={:.4f}'.format(plt_la, roc_auc))\n",
    "\n",
    "    plt.plot([0, 1], [0, 1], color='black', lw=1, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('{}'.format('ROC curve'))\n",
    "    plt.legend(loc=\"lower right\", ncol=1)\n",
    "    plt.grid()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rescale data\n",
    "\n",
    "The ResNet classification model is trained on data standardized by instance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# rescale data\n",
    "X_train, mean_train, std_train = scale_by_instance(X_train * 255.)\n",
    "X_test, mean_test, std_test = scale_by_instance(X_test * 255.)\n",
    "scale = (mean_train, std_train), (mean_test, std_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load pre-trained classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/avl/anaconda3/envs/detect/lib/python3.7/site-packages/tensorflow_core/python/keras/layers/core.py:901: UserWarning:\n",
      "\n",
      "official.vision.image_classification.resnet_cifar_model is not loaded, but a Lambda layer uses it. It may cause errors.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "dataset = 'cifar10'\n",
    "model = 'resnet56'\n",
    "clf = fetch_tf_model(dataset, model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the predictions on the test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9315\n"
     ]
    }
   ],
   "source": [
    "y_pred = predict_batch(X_test, clf, batch_size=32).argmax(axis=1)\n",
    "acc_y_pred = accuracy(y_test, y_pred)\n",
    "print('Accuracy: {:.4f}'.format(acc_y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adversarial attack\n",
    "\n",
    "We investigate both [Carlini-Wagner (C&W)](https://arxiv.org/abs/1608.04644) and [SLIDE](https://arxiv.org/abs/1904.13000) attacks. You can simply load previously found adversarial instances on the pretrained ResNet-56 model. The attacks are generated by using [Foolbox](https://github.com/bethgelab/foolbox):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# C&W attack\n",
    "data_cw = fetch_attack(dataset, model, 'cw')\n",
    "X_train_cw, X_test_cw = data_cw['data_train'], data_cw['data_test']\n",
    "meta_cw = data_cw['meta'] # metadata with hyperparameters of the attack\n",
    "# SLIDE attack\n",
    "data_slide = fetch_attack(dataset, model, 'slide')\n",
    "X_train_slide, X_test_slide = data_slide['data_train'], data_slide['data_test']\n",
    "meta_slide = data_slide['meta']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can verify that the accuracy of the classifier drops to almost $0$%:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: cw 0.0000 -- SLIDE 0.0001\n"
     ]
    }
   ],
   "source": [
    "y_pred_cw = predict_batch(X_test_cw, clf, batch_size=32).argmax(axis=1)\n",
    "y_pred_slide = predict_batch(X_test_slide, clf, batch_size=32).argmax(axis=1)\n",
    "acc_y_pred_cw = accuracy(y_test, y_pred_cw)\n",
    "acc_y_pred_slide = accuracy(y_test, y_pred_slide)\n",
    "print('Accuracy: cw {:.4f} -- SLIDE {:.4f}'.format(acc_y_pred_cw, acc_y_pred_slide))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualise some adversarial instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C&W attack...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SLIDE attack...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot attacked instances\n",
    "idx = [3, 4]\n",
    "print('C&W attack...')\n",
    "plot_adversarial(idx, X_test, y_pred, X_test_cw, y_pred_cw, \n",
    "                 mean_test, std_test, figsize=(10, 10))\n",
    "print('SLIDE attack...')\n",
    "plot_adversarial(idx, X_test, y_pred, X_test_slide, y_pred_slide, \n",
    "                 mean_test, std_test, figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load or train and evaluate the adversarial detector\n",
    "\n",
    "We can again either fetch the pretrained detector from a [Google Cloud Bucket](https://console.cloud.google.com/storage/browser/seldon-models/alibi-detect/ad/cifar10/resnet56) or train one from scratch:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "load_pretrained = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:alibi_detect.ad.adversarialae:No threshold level set. Need to infer threshold using `infer_threshold`.\n"
     ]
    }
   ],
   "source": [
    "filepath = os.path.join(os.getcwd(), 'adversarial')\n",
    "detector_type = 'adversarial'\n",
    "detector_name = 'base'\n",
    "filepath = os.path.join(filepath, detector_name)\n",
    "if load_pretrained:\n",
    "    ad = fetch_detector(filepath, detector_type, dataset, detector_name, model=model)\n",
    "else:  # train detector from scratch\n",
    "    \n",
    "    # define encoder and decoder networks\n",
    "    encoder_net = tf.keras.Sequential(\n",
    "            [\n",
    "                InputLayer(input_shape=(32, 32, 3)),\n",
    "                Conv2D(32, 4, strides=2, padding='same', \n",
    "                       activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2D(64, 4, strides=2, padding='same', \n",
    "                       activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2D(256, 4, strides=2, padding='same', \n",
    "                       activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Flatten(),\n",
    "                Dense(40)\n",
    "            ]\n",
    "        )\n",
    "    \n",
    "    decoder_net = tf.keras.Sequential(\n",
    "        [\n",
    "                InputLayer(input_shape=(40,)),\n",
    "                Dense(4 * 4 * 128, activation=tf.nn.relu),\n",
    "                Reshape(target_shape=(4, 4, 128)),\n",
    "                Conv2DTranspose(256, 4, strides=2, padding='same', \n",
    "                                activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2DTranspose(64, 4, strides=2, padding='same', \n",
    "                                activation=tf.nn.relu, kernel_regularizer=l1(1e-5)),\n",
    "                Conv2DTranspose(3, 4, strides=2, padding='same', \n",
    "                                activation=None, kernel_regularizer=l1(1e-5))\n",
    "            ]\n",
    "        )\n",
    "    \n",
    "    # initialise and train detector\n",
    "    ad = AdversarialAE(\n",
    "        encoder_net=encoder_net, \n",
    "        decoder_net=decoder_net, \n",
    "        model=clf\n",
    "    )\n",
    "    ad.fit(X_train, epochs=40, batch_size=64, verbose=True)\n",
    "    \n",
    "    # save the trained adversarial detector\n",
    "    save_detector(ad, filepath)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The detector first reconstructs the input instances which can be adversarial. The reconstructed input is then fed to the classifier to compute the adversarial score. If the score is above a threshold, the instance is classified as adversarial and the detector tries to correct the attack. Let's investigate what happens when we reconstruct attacked instances and make predictions on them:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_recon_cw = predict_batch(X_test_cw, ad.ae, batch_size=32)\n",
    "X_recon_slide = predict_batch(X_test_slide, ad.ae, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_recon_cw = predict_batch(X_recon_cw, clf, batch_size=32).argmax(axis=1)\n",
    "y_recon_slide = predict_batch(X_recon_slide, clf, batch_size=32).argmax(axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy on attacked vs. reconstructed instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy after C&W attack 0.0000 -- reconstruction 0.8048\n",
      "Accuracy after SLIDE attack 0.0001 -- reconstruction 0.8159\n"
     ]
    }
   ],
   "source": [
    "acc_y_recon_cw = accuracy(y_test, y_recon_cw)\n",
    "acc_y_recon_slide = accuracy(y_test, y_recon_slide)\n",
    "print('Accuracy after C&W attack {:.4f} -- reconstruction {:.4f}'.format(acc_y_pred_cw, acc_y_recon_cw))\n",
    "print('Accuracy after SLIDE attack {:.4f} -- reconstruction {:.4f}'.format(acc_y_pred_slide, acc_y_recon_slide))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The detector restores the accuracy after the attacks from almost $0$% to well over $80$%! We can compute the adversarial scores and inspect some of the reconstructed instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "score_x = ad.score(X_test, batch_size=32)\n",
    "score_cw = ad.score(X_test_cw, batch_size=32)\n",
    "score_slide = ad.score(X_test_slide, batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "code_folding": [
     0
    ],
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C&W attack...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x1080 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n",
      "WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SLIDE attack...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x1080 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualize original, attacked and reconstructed instances with adversarial scores\n",
    "print('C&W attack...')\n",
    "idx = [10, 13, 14, 16, 17]\n",
    "plot_adversarial(idx, X_test, y_pred, X_test_cw, y_pred_cw, mean_test, std_test, \n",
    "                 score_x=score_x, score_x_adv=score_cw, X_recon=X_recon_cw, \n",
    "                 y_recon=y_recon_cw, figsize=(10, 15))\n",
    "print('SLIDE attack...')\n",
    "idx = [23, 25, 27, 29, 34]\n",
    "plot_adversarial(idx, X_test, y_pred, X_test_slide, y_pred_slide, mean_test, std_test, \n",
    "                 score_x=score_x, score_x_adv=score_slide, X_recon=X_recon_slide, \n",
    "                 y_recon=y_recon_slide, figsize=(10, 15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ROC curves and AUC values show the effectiveness of the adversarial score to detect adversarial instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot roc curve\n",
    "roc_data = {\n",
    "    'original': {'scores': score_x, 'predictions': y_pred},\n",
    "    'C&W': {'scores': score_cw, 'predictions': y_pred_cw, 'normal': 'original'},\n",
    "    'SLIDE': {'scores': score_slide, 'predictions': y_pred_slide, 'normal': 'original'}\n",
    "}\n",
    "\n",
    "plot_roc(roc_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The threshold for the adversarial score can be set via ```infer_threshold```. We need to pass a batch of instances $X$ and specify what percentage of those we consider to be normal via `threshold_perc`. Assume we have only normal instances some of which the model has misclassified leading to a higher score if the reconstruction picked up features from the correct class or some might look adversarial in the first place. As a result, we set our threshold at $95$%:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adversarial threshold: 2.6722\n"
     ]
    }
   ],
   "source": [
    "ad.infer_threshold(X_test, threshold_perc=95, margin=0., batch_size=32)\n",
    "print('Adversarial threshold: {:.4f}'.format(ad.threshold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `correct` method of the detector executes the diagram in Figure 1. First the adversarial scores is computed. For instances where the score is above the threshold, the classifier prediction on the reconstructed instance is returned. Otherwise the original prediction is kept. The method returns a dictionary containing the metadata of the detector, whether the instances in the batch are adversarial (above the threshold) or not, the classifier predictions using the correction mechanism and both the original and reconstructed predictions. Let's illustrate this on a batch containing some adversarial (C&W) and original test set instances:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 32, 32, 3) (2000,)\n"
     ]
    }
   ],
   "source": [
    "n_test = X_test.shape[0]\n",
    "np.random.seed(0)\n",
    "idx_normal = np.random.choice(n_test, size=1600, replace=False)\n",
    "idx_cw = np.random.choice(n_test, size=400, replace=False)\n",
    "\n",
    "X_mix = np.concatenate([X_test[idx_normal], X_test_cw[idx_cw]])\n",
    "y_mix = np.concatenate([y_test[idx_normal], y_test[idx_cw]])\n",
    "print(X_mix.shape, y_mix.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check the model performance:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.7380\n"
     ]
    }
   ],
   "source": [
    "y_pred_mix = predict_batch(X_mix, clf, batch_size=32).argmax(axis=1)\n",
    "acc_y_pred_mix = accuracy(y_mix, y_pred_mix)\n",
    "print('Accuracy {:.4f}'.format(acc_y_pred_mix))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This can be improved with the correction mechanism:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy 0.8205\n"
     ]
    }
   ],
   "source": [
    "preds = ad.correct(X_mix, batch_size=32)\n",
    "acc_y_corr_mix = accuracy(y_mix, preds['data']['corrected'])\n",
    "print('Accuracy {:.4f}'.format(acc_y_corr_mix))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are a few other tricks highlighted in the [paper](https://arxiv.org/abs/2002.09364) (**temperature scaling** and **hidden layer K-L divergence**) and implemented in Alibi Detect which can further boost the adversarial detector's performance. Check [this example notebook](https://docs.seldon.io/projects/alibi-detect/en/stable/examples/ad_ae_cifar10.html) for more details."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Drift detection with Kolmogorov-Smirnov\n",
    "\n",
    "### Method\n",
    "\n",
    "The drift detector applies feature-wise two-sample [Kolmogorov-Smirnov](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test) (K-S) tests. For multivariate data, the obtained p-values for each feature are aggregated either via the [Bonferroni](https://mathworld.wolfram.com/BonferroniCorrection.html) or the [False Discovery Rate](http://www.math.tau.ac.il/~ybenja/MyPapers/benjamini_hochberg1995.pdf) (FDR) correction. The Bonferroni correction is more conservative and controls for the probability of at least one false positive. The FDR correction on the other hand allows for an expected fraction of false positives to occur.\n",
    "\n",
    "For high-dimensional data, we typically want to reduce the dimensionality before computing the feature-wise univariate K-S tests and aggregating those via the chosen correction method. Following suggestions in [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://arxiv.org/abs/1810.11953), we incorporate Untrained AutoEncoders (UAE), black-box shift detection using the classifier's softmax outputs ([BBSDs](https://arxiv.org/abs/1802.03916)) and [PCA](https://en.wikipedia.org/wiki/Principal_component_analysis) as out-of-the box preprocessing methods. Preprocessing methods which do not rely on the classifier will usually pick up drift in the input data, while BBSDs focuses on label shift. The [adversarial detector](https://arxiv.org/abs/2002.09364) which is part of the library can also be transformed into a drift detector picking up drift that reduces the performance of the classification model. We can therefore combine different preprocessing techniques to figure out if there is drift which hurts the model performance, and whether this drift can be classified as input drift or label shift.\n",
    "\n",
    "Note that the library also has a drift detector based on the [Maximum Mean Discrepancy](https://docs.seldon.io/projects/alibi-detect/en/stable/examples/cd_mmd_cifar10.html#) and contains [drift on text functionality](https://docs.seldon.io/projects/alibi-detect/en/stable/examples/cd_text_imdb.html) as well.\n",
    "\n",
    "\n",
    "### Dataset\n",
    "\n",
    "We will use the CIFAR-10-C dataset ([Hendrycks & Dietterich, 2019](https://arxiv.org/abs/1903.12261)) to evaluate the drift detector. The instances in\n",
    "CIFAR-10-C come from the test set in CIFAR-10 but have been corrupted and perturbed by various types of noise, blur, brightness etc. at different levels of severity, leading to a gradual decline in the classification model performance. We also check for drift against the original test set with class imbalances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# yet again import stuff\n",
    "from alibi_detect.cd import KSDrift\n",
    "from alibi_detect.cd.preprocess import UAE, HiddenOutput\n",
    "from alibi_detect.datasets import fetch_cifar10c, corruption_types_cifar10c"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can select from the following corruption types at 5 severity levels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['brightness', 'contrast', 'defocus_blur', 'elastic_transform', 'fog', 'frost', 'gaussian_blur', 'gaussian_noise', 'glass_blur', 'impulse_noise', 'jpeg_compression', 'motion_blur', 'pixelate', 'saturate', 'shot_noise', 'snow', 'spatter', 'speckle_noise', 'zoom_blur']\n"
     ]
    }
   ],
   "source": [
    "corruptions = corruption_types_cifar10c()\n",
    "print(corruptions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's pick a subset of the corruptions at corruption level 5. Each corruption type consists of perturbations on all of the original test set images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "corruption = ['gaussian_noise', 'motion_blur', 'brightness', 'pixelate']\n",
    "X_corr, y_corr = fetch_cifar10c(corruption=corruption, severity=5, return_X_y=True)\n",
    "X_corr = X_corr.astype('float32') / 255"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We split the original test set in a reference dataset and a dataset which should not be rejected under the *H<sub>0</sub>* of the K-S test. We also split the corrupted data by corruption type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5000, 32, 32, 3) (5000, 32, 32, 3)\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "n_test = X_test.shape[0]\n",
    "idx = np.random.choice(n_test, size=n_test // 2, replace=False)\n",
    "idx_h0 = np.delete(np.arange(n_test), idx, axis=0)\n",
    "X_ref,y_ref = X_test[idx], y_test[idx]\n",
    "X_h0, y_h0 = X_test[idx_h0], y_test[idx_h0]\n",
    "print(X_ref.shape, X_h0.shape)\n",
    "\n",
    "X_c = []\n",
    "n_corr = len(corruption)\n",
    "for i in range(n_corr):\n",
    "    X_c.append(scale_by_instance(X_corr[i * n_test:(i + 1) * n_test])[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualise the same instance for each corruption type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot original and corrupted images\n",
    "i = 1\n",
    "\n",
    "n_test = X_test.shape[0]\n",
    "plt.title('Original')\n",
    "plt.axis('off')\n",
    "plt.imshow((X_test[i] * std_test[i] + mean_test[i]) / 255.)\n",
    "plt.show()\n",
    "for _ in range(len(corruption)):\n",
    "    plt.title(corruption[_])\n",
    "    plt.axis('off')\n",
    "    plt.imshow(X_corr[n_test * _+ i])\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also verify that the performance of a ResNet-32 classification model on CIFAR-10 drops significantly on this perturbed dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy:\n",
      "Original 0.9278\n",
      "gaussian_noise 0.2208\n",
      "motion_blur 0.6339\n",
      "brightness 0.8913\n",
      "pixelate 0.3666\n"
     ]
    }
   ],
   "source": [
    "dataset = 'cifar10'\n",
    "model = 'resnet32'\n",
    "clf = fetch_tf_model(dataset, model)\n",
    "acc = clf.evaluate(X_test, y_test, batch_size=128, verbose=0)[1]\n",
    "print('Test set accuracy:')\n",
    "print('Original {:.4f}'.format(acc))\n",
    "clf_accuracy = {'original': acc}\n",
    "for _ in range(len(corruption)):\n",
    "    acc = clf.evaluate(X_c[_], y_test, batch_size=128, verbose=0)[1]\n",
    "    clf_accuracy[corruption[_]] = acc\n",
    "    print('{} {:.4f}'.format(corruption[_], acc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Given the drop in performance, it is important that we detect the harmful data drift!\n",
    "\n",
    "### Detect drift\n",
    "\n",
    "We are trying to detect data drift on high-dimensional (*32x32x3*) data using an aggregation of univariate K-S tests. It therefore makes sense to apply dimensionality reduction first. Some dimensionality reduction methods also used in [Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift](https://arxiv.org/pdf/1810.11953.pdf) are readily available: **UAE** (Untrained AutoEncoder), **BBSDs** (black-box shift detection using the classifier's softmax outputs) and **PCA** (using `scikit-learn`). \n",
    "\n",
    "#### Untrained AutoEncoder\n",
    "\n",
    "First we try UAE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.random.set_seed(0)\n",
    "\n",
    "# define encoder\n",
    "encoding_dim = 32\n",
    "encoder_net = tf.keras.Sequential(\n",
    "  [\n",
    "      InputLayer(input_shape=(32, 32, 3)),\n",
    "      Conv2D(64, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "      Conv2D(128, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "      Conv2D(512, 4, strides=2, padding='same', activation=tf.nn.relu),\n",
    "      Flatten(),\n",
    "      Dense(encoding_dim,)\n",
    "  ]\n",
    ")\n",
    "uae = UAE(encoder_net=encoder_net)\n",
    "preprocess_kwargs = {'model': uae, 'batch_size': 128}\n",
    "\n",
    "# initialise drift detector\n",
    "p_val = .05\n",
    "cd = KSDrift(\n",
    "    p_val=p_val,        # p-value for K-S test \n",
    "    X_ref=X_ref,       # test against original test set\n",
    "    preprocess_kwargs=preprocess_kwargs\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check whether the detector thinks drift occurred within the original test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drift? No!\n"
     ]
    }
   ],
   "source": [
    "preds_h0 = cd.predict(X_h0, return_p_val=True)\n",
    "print('Drift? {}'.format(labels[preds_h0['data']['is_drift']]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, no drift occurred. We can also inspect the feature-wise K-S statistics, threshold value and p-values for each univariate K-S test by (encoded) feature before the multivariate correction. Most of them are well above the $0.05$ threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K-S statistics:\n",
      "[0.017  0.013  0.0124 0.0234 0.0156 0.0144 0.025  0.0144 0.0216 0.0112\n",
      " 0.0122 0.0146 0.012  0.0154 0.015  0.0148 0.012  0.0158 0.0104 0.0144\n",
      " 0.0116 0.0148 0.0194 0.0112 0.0176 0.0154 0.0152 0.0158 0.0166 0.0202\n",
      " 0.0208 0.0196]\n",
      "\n",
      "K-S statistic threshold: 0.0015625\n",
      "\n",
      "p-values:\n",
      "[0.46531922 0.79201305 0.8367454  0.12939005 0.5769981  0.677735\n",
      " 0.08786641 0.677735   0.19387017 0.912423   0.850771   0.66088647\n",
      " 0.8642828  0.5936282  0.62716705 0.6440195  0.8642828  0.5604951\n",
      " 0.94969434 0.677735   0.88960564 0.6440195  0.30355498 0.912423\n",
      " 0.420929   0.5936282  0.61036026 0.5604951  0.496191   0.25943416\n",
      " 0.22956407 0.2920585 ]\n"
     ]
    }
   ],
   "source": [
    "# print stats for H0\n",
    "print('K-S statistics:')\n",
    "print(preds_h0['data']['distance'])\n",
    "print(f\"\\nK-S statistic threshold: {preds_h0['data']['threshold']}\")\n",
    "print('\\np-values:')\n",
    "print(preds_h0['data']['p_val'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now check the predictions on the perturbed data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corruption type: gaussian_noise\n",
      "Drift? Yes!\n",
      "Feature-wise p-values:\n",
      "[1.3873853e-04 2.6511249e-01 9.4417885e-02 3.8390055e-01 7.1598392e-04\n",
      " 5.9501763e-04 1.7882723e-02 6.8460542e-01 2.9989053e-02 3.7781857e-02\n",
      " 6.6516680e-01 3.5206401e-07 4.4194090e-05 7.9351515e-03 6.5543061e-01\n",
      " 1.0220607e-04 1.9904150e-01 8.7863362e-01 1.1572546e-02 8.7863362e-01\n",
      " 2.7950103e-03 1.1150700e-02 3.6837953e-01 4.4130555e-01 7.8937606e-05\n",
      " 5.1594800e-03 6.2871156e-03 1.3455943e-03 9.7143359e-02 1.4105450e-07\n",
      " 1.7258449e-02 1.4648294e-01]\n",
      "\n",
      "Corruption type: motion_blur\n",
      "Drift? Yes!\n",
      "Feature-wise p-values:\n",
      "[3.75149103e-13 1.34077845e-02 2.96938539e-01 1.38920277e-01\n",
      " 5.03090501e-01 1.71172902e-01 6.25065863e-02 5.15948003e-03\n",
      " 4.85032678e-01 2.16893386e-03 7.48323351e-02 1.28741434e-03\n",
      " 1.24597345e-02 2.57197134e-05 1.32995670e-09 7.83811294e-10\n",
      " 7.93515146e-03 3.10395747e-01 2.09074125e-01 5.97174764e-01\n",
      " 5.95017627e-04 9.71433595e-02 3.38559955e-01 3.31362933e-01\n",
      " 7.15983915e-04 3.45860541e-01 5.68405211e-01 2.43121485e-06\n",
      " 5.30714750e-01 2.57197134e-05 1.80098459e-01 8.85808766e-01]\n",
      "\n",
      "Corruption type: brightness\n",
      "Drift? Yes!\n",
      "Feature-wise p-values:\n",
      "[4.8499879e-07 1.3873853e-04 1.1572546e-02 1.6691783e-10 2.7127105e-01\n",
      " 2.5326056e-02 1.6036824e-03 4.6618203e-05 2.1745481e-04 1.2474350e-01\n",
      " 6.2343346e-14 3.1785589e-08 8.9277834e-01 3.8390055e-01 8.2942984e-12\n",
      " 8.9514272e-09 2.4149875e-01 3.5845602e-03 2.2287663e-29 5.2143931e-01\n",
      " 2.2484422e-01 2.7950277e-16 1.9522957e-05 1.0870906e-01 7.6357084e-03\n",
      " 3.2130507e-04 3.4248088e-02 4.7411124e-14 2.5942018e-18 1.5351619e-03\n",
      " 4.6728885e-01 2.2888689e-06]\n",
      "\n",
      "Corruption type: pixelate\n",
      "Drift? Yes!\n",
      "Feature-wise p-values:\n",
      "[3.0361506e-01 8.5970649e-04 1.6684313e-01 7.0512637e-02 5.0309050e-01\n",
      " 7.0512637e-02 2.5905645e-01 2.2484422e-01 8.9953583e-01 3.5326433e-01\n",
      " 5.4005289e-01 3.3136293e-01 2.1316923e-02 3.9982069e-01 1.4057782e-06\n",
      " 7.9374194e-02 3.7114436e-04 7.7923602e-01 2.7127105e-01 2.5905645e-01\n",
      " 8.9953583e-01 1.6260068e-01 2.8040027e-02 9.1756582e-02 1.9192154e-02\n",
      " 1.7117290e-01 7.1357483e-01 2.9989053e-02 3.6077085e-01 1.2459734e-02\n",
      " 4.2442977e-01 2.6461019e-04]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# print stats for corrupted data\n",
    "for x, c in zip(X_c, corruption):\n",
    "    preds = cd.predict(x, return_p_val=True)\n",
    "    print(f'Corruption type: {c}')\n",
    "    print('Drift? {}'.format(labels[preds['data']['is_drift']]))\n",
    "    print('Feature-wise p-values:')\n",
    "    print(preds['data']['p_val'])\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### BBSDs\n",
    "\n",
    "For **BBSDs**, we use the classifier's softmax outputs for black-box shift detection. This method is based on [Detecting and Correcting for Label Shift with Black Box Predictors](https://arxiv.org/abs/1802.03916).\n",
    "\n",
    "Here we use the output of the softmax layer to detect the drift, but other hidden layers can be extracted as well by setting *'layer'* to the index of the desired hidden layer in the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# use output softmax layer\n",
    "preprocess_kwargs = {'model': HiddenOutput(model=clf, layer=-1), 'batch_size': 128}\n",
    "\n",
    "cd = KSDrift(\n",
    "    p_val=p_val,\n",
    "    X_ref=X_ref,\n",
    "    preprocess_kwargs=preprocess_kwargs\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is again no drift on the original held out test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drift? No!\n",
      "\n",
      "p-values:\n",
      "[0.11774229 0.52796143 0.19387017 0.20236294 0.496191   0.72781175\n",
      " 0.12345381 0.420929   0.8367454  0.7604178 ]\n"
     ]
    }
   ],
   "source": [
    "preds_h0 = cd.predict(X_h0)\n",
    "print('Drift? {}'.format(labels[preds_h0['data']['is_drift']]))\n",
    "print('\\np-values:')\n",
    "print(preds_h0['data']['p_val'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We compare this with the perturbed data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corruption type: gaussian_noise\n",
      "Drift? Yes!\n",
      "Feature-wise p-values:\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "\n",
      "Corruption type: motion_blur\n",
      "Drift? Yes!\n",
      "Feature-wise p-values:\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "\n",
      "Corruption type: brightness\n",
      "Drift? Yes!\n",
      "Feature-wise p-values:\n",
      "[0.0000000e+00 4.2024049e-15 2.8963613e-33 4.8499879e-07 2.3718185e-15\n",
      " 1.2473309e-05 2.9714003e-30 1.0611427e-09 4.6048109e-12 4.1857830e-17]\n",
      "\n",
      "Corruption type: pixelate\n",
      "Drift? Yes!\n",
      "Feature-wise p-values:\n",
      "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for x, c in zip(X_c, corruption):\n",
    "    preds = cd.predict(x)\n",
    "    print(f'Corruption type: {c}')\n",
    "    print('Drift? {}'.format(labels[preds['data']['is_drift']]))\n",
    "    print('Feature-wise p-values:')\n",
    "    print(preds['data']['p_val'])\n",
    "    print('')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more functionality and examples, such as updating the reference data with reservoir sampling or picking another multivariate correction mechanism, check out [this example notebook](https://docs.seldon.io/projects/alibi-detect/en/stable/examples/cd_ks_cifar10.html).\n",
    "\n",
    "### Leveraging the adversarial detector for malicious drift detection\n",
    "\n",
    "While monitoring covariate and predicted label shift is all very interesting and exciting, at the end of the day we are mainly interested in whether the drift actually hurt the model performance significantly. To this end, we can leverage the adversarial detector and measure univiariate drift on the adversarial scores! "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(0)\n",
    "idx = np.random.choice(n_test, size=n_test // 2, replace=False)\n",
    "X_ref = scale_by_instance(X_test[idx])[0]\n",
    "\n",
    "cd = KSDrift(\n",
    "    p_val=.05,\n",
    "    X_ref=X_ref,\n",
    "    preprocess_fn=ad.score,  # adversarial score fn = preprocess step\n",
    "    preprocess_kwargs={'batch_size': 128}\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make drift predictions on the original test set and corrupted data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "code_folding": [
     0
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H0: Accuracy 0.9286 -- Drift? No!\n",
      "gaussian_noise: Accuracy 0.2208 -- Drift? Yes!\n",
      "motion_blur: Accuracy 0.6339 -- Drift? Yes!\n",
      "brightness: Accuracy 0.8913 -- Drift? Yes!\n",
      "pixelate: Accuracy 0.3666 -- Drift? Yes!\n"
     ]
    }
   ],
   "source": [
    "# evaluate classifier on different datasets\n",
    "clf_accuracy['h0'] = clf.evaluate(X_h0, y_h0, batch_size=128, verbose=0)[1]\n",
    "preds_h0 = cd.predict(X_h0)\n",
    "print('H0: Accuracy {:.4f} -- Drift? {}'.format(\n",
    "    clf_accuracy['h0'], labels[preds_h0['data']['is_drift']]))\n",
    "for x, c in zip(X_c, corruption):\n",
    "    preds = cd.predict(x)\n",
    "    print('{}: Accuracy {:.4f} -- Drift? {}'.format(\n",
    "        c, clf_accuracy[c],labels[preds['data']['is_drift']]))"
   ]
  },
  {
   "attachments": {
    "adversarialscores.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can therefore **use the scores of the detector itself to quantify the harmfulness of the drift**! We can generalise this to all the corruptions at each severity level in CIFAR-10-C.\n",
    "\n",
    "On the plot below we show the mean values and standard deviations of the adversarial scores per severity level. The plot shows the mean adversarial scores (lhs) and ResNet-32 accuracies (rhs) for increasing data corruption severity levels. Level 0 corresponds to the original test set. Harmful scores  are scores from instances which have been flipped from the correct to an incorrect prediction because of the corruption. Not harmful means that the prediction was unchanged after the corruption. The chart can be reproduced in [this notebook](https://docs.seldon.io/projects/alibi-detect/en/stable/examples/cd_ks_cifar10.html).\n",
    "\n",
    "![adversarialscores.png](attachment:adversarialscores.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deploy\n",
    "\n",
    "We can deploy the drift detector in a similar fashion as the [outlier detector](#od_deploy). For a more detailed step-by-step overview of the deployment process, check [this notebook](https://docs.seldon.io/projects/seldon-core/en/stable/examples/drift_cifar10.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "drift-detector.default.example.com\n"
     ]
    }
   ],
   "source": [
    "SERVICE_HOSTNAMES=!(kubectl get ksvc drift-detector -o jsonpath='{.status.url}' | cut -d \"/\" -f 3)\n",
    "SERVICE_HOSTNAME_CD=SERVICE_HOSTNAMES[0]\n",
    "print(SERVICE_HOSTNAME_CD)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The deployed drift detector accumulates requests until a predefined `drift_batch_size` is reached, in our case $5000$ which is defined in the [*yaml* for the deployment](https://github.com/SeldonIO/seldon-core/blob/master/components/drift-detection/cifar10/cifar10.yaml) and set in the [drift detector wrapper](https://github.com/SeldonIO/seldon-core/blob/master/components/alibi-detect-server/adserver/cd_model.py). After $5000$ instances, the batch is cleared and fills up again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a0a2b42ff1184e51b084087e891bb086",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=50.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Drift? No!\n"
     ]
    }
   ],
   "source": [
    "from tqdm.notebook import tqdm\n",
    "\n",
    "drift_batch_size = 5000\n",
    "\n",
    "# accumulate batches\n",
    "for i in tqdm(range(0, drift_batch_size, 100)):\n",
    "    x = X_h0[i:i+100]\n",
    "    predict(x)\n",
    "\n",
    "# check message dumper\n",
    "res=!kubectl logs $(kubectl get pod -l serving.knative.dev/configuration=message-dumper-drift -o jsonpath='{.items[0].metadata.name}') user-container\n",
    "data= []\n",
    "for i in range(0,len(res)):\n",
    "    if res[i] == 'Data,':\n",
    "        data.append(res[i+1])\n",
    "j = json.loads(json.loads(data[0]))\n",
    "print(\"Drift?\", labels[j[\"data\"][\"is_drift\"]==1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now run the same test on some corrupted data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Corruption: gaussian_noise\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53cb25da3ffa4a7c87eb6312c1347567",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=50.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Drift? Yes!\n"
     ]
    }
   ],
   "source": [
    "c = 0\n",
    "\n",
    "print(f'Corruption: {corruption[c]}')\n",
    "\n",
    "# accumulate batches\n",
    "for i in tqdm(range(0, drift_batch_size, 100)):\n",
    "    x = X_c[c][i:i+100]\n",
    "    predict(x)\n",
    "\n",
    "# check message dumper\n",
    "res=!kubectl logs $(kubectl get pod -l serving.knative.dev/configuration=message-dumper-drift -o jsonpath='{.items[0].metadata.name}') user-container\n",
    "data= []\n",
    "for i in range(0,len(res)):\n",
    "    if res[i] == 'Data,':\n",
    "        data.append(res[i+1])\n",
    "j = json.loads(json.loads(data[1]))\n",
    "print(\"Drift?\", labels[j[\"data\"][\"is_drift\"]==1])"
   ]
  }
 ],
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